Nowadays, there is an ever growing interest for gas turbine and aeroengines prognostics. The capability to assess not only the current state of an asset, but also to be able to predict its remaining useful life (RUL), and hence to perform condition-based maintenance (CBM) —if, and only when, it is needed— can represent a huge deal in the manufacturer profits. Against the plethora of data-driven methods that have arisen in the past few years, there is still some knowledge to be gained in terms of understanding the underlying phenomenology of engine degradation. In fact, it is certainly a non-trivial problem, to realize what has happened to the rotating components of an engine just by observing the pressure being measured by certain sensor rise, or some other temperature measured along the main gas-path decrease its value. In this regard, model-based approaches —and, in particular, gas path analysis (GPA)— can assist us in gaining such knowledge. In this paper, a non-linear GPA technique is revisited, introducing some novelties to the solver, and making use of current computational methods and resources, to establish a solid ‘foundation’ that will serve as the basis for further research.
A reduced order model is developed to monitor aeroengines condition (defining their degradation from a baseline state) in real-time, by using data collected in specific sensors. This reduced model is constructed by applying higher order singular value decomposition plus interpolation to appropriate data, organized in tensor form. Such data are obtained using a detailed engine model that takes the engine physics into account. Thus, the method synergically combines the advantages of data-driven (fast online operation) and model-based (the engine physics is accounted for) condition monitoring methods. Using this reduced order model as surrogate of the engine model, two gradient-like condition monitoring tools are constructed. The first tool is extremely fast and able to precisely compute `on the fly’ the turbine inlet temperature, which is a paramount parameter for the engine performance, operation, and maintenance, and can only be roughly estimated by the engine instrumentation in civil aviation. The second tool is not so fast (but still reasonably inexpensive) and precisely computes both, the engine degradation and the turbine inlet temperature at which sensors data have been acquired. These tools are robust in connection with random noise added to the sensors data and can be straight forwardly applied to other mechanical systems.
A reduced order model is developed to monitor aeroengines condition (defining their degradation from a baseline state) in real-time, by using data collected in specific sensors. This reduced model is constructed by applying higher order singular value decomposition plus interpolation to appropriate data, organized in tensor form. Such data are obtained using a detailed engine model that takes the engine physics into account. Thus, the method synergically combines the advantages of data-driven (fast online operation) and model-based (the engine physics is accounted for) condition monitoring methods. Using this reduced order model as surrogate of the engine model, two gradient-like condition monitoring tools are constructed. The first tool is extremely fast and able to precisely compute `on the fly’ the turbine inlet temperature, which is a paramount parameter for the engine performance, operation, and maintenance, and can only be roughly estimated by the engine instrumentation in civil aviation. The second tool is not so fast (but still reasonably inexpensive) and precisely computes both, the engine degradation and the turbine inlet temperature at which sensors data have been acquired. These tools are robust in connection with random noise added to the sensors data and can be straight forwardly applied to other mechanical systems.
In this work, we study numerically with large eddy simulation, the effects induced by the three-dimensional geometry of the channel on the flow topology that exists when the three-dimensional intrinsic instabilities appear in a backward facing step flow with low aspect ratio for Reynolds in the transitional regime (Re ¼ 1,000-1,600), and its impact on the heat flux in the lower wall. Under the transitional regime, the three-dimensional instabilities begin to appear, but they can be masked by the flows due to the presence of the side walls. The study is carried out with two boundary conditions in the sidewalls, slip, and no-slip, to discriminate between the three-dimensionality induced by the geometry and the intrinsic threedimensional instabilities. The results obtained are compared between the two boundary conditions, establishing what type of flow prevails and its influence on time-averaged mean Nusselt number for all Reynolds.
Flow-induced vibrations of rigid prisms supported elastically were studied experimentally in a free-surface water channel with a high blockage (2/5). The study focused on finding the prism cross-sectional shape that maximizes the efficiency of energy harvesting. Seven cross-sectional shapes were tested: square, circular, 45° tilted square, equilateral triangle, isosceles 120° triangle, D-section, and C-section. All other dimensionless parameters of the problem, mass ratio, damping, blockage ratio, reduced velocity range, and the Reynolds (Re) number (characteristic velocity times characteristic length divided by kinematic viscosity) range (400–1070), were kept unchanged. By doing so, the effect of the cross-sectional shape was isolated. D-section proved to be the geometry with the highest values of energy transfer efficiency. A hysteresis loop was present in its oscillatory response (dimensionless oscillation amplitude vs reduced velocity). This loop was characterized by two branches, (+) and (−), meaning a bi-valued amplitude response for each reduced velocity. Regarding temporal patterns of wake topology and body motion, it was found that synchronization occurs in the (+) branch, but not in the (−). Regarding vortex shedding modes, particle image velocimetry was used for identification purposes, and it was found that the 2P mode is the dominant mode in the (+) branch, while the 2S mode pervades the (−). Finally, a new relative reduced velocity definition was introduced, and, when re-plotting the experimental results, it was found that the hysteresis loop disappears, thereby providing a more compact mathematical description of the observed phenomena.
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