Predicting Blade Leading Edge Erosion in an Axial Induced Draft Fan

Corsini, Alessandro; Marchegiani, Andrea; Rispoli, Franco; Venturini, Paolo; Sheard, Anthony G.

doi:10.1115/1.4004724

Cited by 30 publications

(8 citation statements)

References 21 publications

(33 reference statements)

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“…In previous studies, [25][26][27][28] for instance, we modeled and studied the particle deposition process in turbine and compressor blades and cooling passages, with and without the effect of temperature. Papers [29][30][31][32] were focused on modeling of solid particle erosion: In these papers, we studied the erosion phenomenon in relation to sand or coal fly ash, paying particular attention to blades of industrial fans. In 2015, we presented an approach to evaluate the severity of rain erosion damage in multi-MW wind turbine based on the water hammer pressure exerted during a droplet impact.…”

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confidence: 99%

Machine learnt prediction method for rain erosion damage on wind turbine blades

et al. 2021

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This paper proposes a paradigm shift in the numerical simulation approach to predict rain erosion damage on wind turbine blades, given the blade geometry, its coating material, and the atmospheric conditions (wind and rain) expected at the installation site. Contrary to what has been done so far, numerical simulations (flow field and particle tracking) are used not to study a specific (wind and rain) operating condition but to build a large database of possible operating conditions of the blade section. A machine learning algorithm, trained on this database, defines a prediction module that gives the feature of the impact pattern over the 2-D section, given the wind and rain flow. The advantage of this approach is that the prediction becomes much faster than using the standard simulations; thus, the study of a large set of variable operating conditions becomes possible. The module, coupled with an erosion model, is used to compute the erosion damage of the blade working on specific installation site. In this way, the variations of the flow conditions due to dynamic effects such as variable wind, wind turbulence, and turbine control can be also considered in the erosion computation. Here, we describe the method, the database creation, and the development of the prediction tool. Then, the method is applied to predict the erosion damage on a blade section of a reference wind turbine, after one year of operation in a rainy onshore site. Results are in good agreement with on field observations, showing the potential of the approach.

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Machine learnt prediction method for rain erosion damage on wind turbine blades

et al. 2021

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“…Because of their nonspherical and angular shapes [18,19], VA particles are highly abrasive. This leads to erosion of forward facing surfaces (fan, compressor blades, and fuel spray nozzles) during their transition through the engine (mainly fan and compressor stages), which leads to a loss of compressor aerodynamic efficiency [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Volcanic Ash Ingestion Into a Large Aero Engine: Particle–Fan Interactions

Vogel

Durant

Cassiani

et al. 2018

Journal of Turbomachinery

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Volcanic ash (VA) clouds in flight corridors present a significant threat to aircraft operations as VA particles can cause damage to gas turbine engine components that lead to a reduction of engine performance and compromise flight safety. In the last decade, research has mainly focused on processes such as erosion of compressor blades and static components caused by impinging ash particles as well as clogging and/or corrosion effects of soft or molten ash particles on hot section turbine airfoils and components. However, there is a lack of information on how the fan separates ingested VA particles from the core stream flow into the bypass flow and therefore influences the mass concentration inside the engine core section, which is most vulnerable and critical for safety. In this numerical simulation study, we investigated the VA particle–fan interactions and resulting reductions in particle mass concentrations entering the engine core section as a function of particle size, fan rotation rate, and for two different flight altitudes. For this, we used a high-bypass gas-turbine engine design, with representative intake, fan, spinner, and splitter geometries for numerical computational fluid dynamics (CFD) simulations including a Lagrangian particle-tracking algorithm. Our results reveal that particle–fan interactions redirect particles from the core stream flow into the bypass stream tube, which leads to a significant particle mass concentration reduction inside the engine core section. The results also show that the particle–fan interactions increase with increasing fan rotation rates and VA particle size. Depending on ingested VA size distributions, the particle mass inside the engine core flow can be up to 30% reduced compared to the incoming particle mass flow. The presented results enable future calculations of effective core flow exposure or dosages based on simulated or observed atmospheric VA particle size distribution, which is required to quantify engine failure mechanisms after exposure to VA. As an example, we applied our methodology to a recent aircraft encounter during the Mt. Kelud 2014 eruption. Based on ambient VA concentrations simulated with an atmospheric particle dispersion model (FLEXPART), we calculated the effective particle mass concentration inside the core stream flow along the actual flight track and compared it with the whole engine exposure.

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“…Here we adopted this statistical approach, namely the Particle Cloud Tracking (PCT) model, first proposed by Baxter [23], and then developed and used by a number of researches [24][25][26][27][28][29]. Although the SPT provides more accurate results, the computational effort required is between one and two orders of magnitude larger than that required for PCT, thus for industrial applications the PCT is more suitable [28][29][30][31]. The cloud equation of motion is the ensemble averaged version of the BBO equation, reduced in accordance to the application's constraints [23].…”

Section: Modelling Approachmentioning

confidence: 99%

Predicting the Performance of an Industrial Centrifugal Fan Incorporating Cambered Plate Impeller Blades

Cardillo¹,

Corsini²,

Delibra³

et al. 2014

Period. Polytech. Mech. Eng.

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Application of computational methods to industrial fan de- IntroductionThe industrial centrifugal fans used in cement, steel and power applications are typically tailored to the applications' specific flow and pressure demands [1]. Although aerodynamic performance has historically been scaled from previous laboratory test data, the impeller, shaft and housing mechanical design, bearing selection and rotor-dynamic analysis are invariably unique. A consequence of the need for a unique design for each fan has resulted in a historic focus on automating the processes associated with mechanical design and rotor-dynamic analysis [1]. Design methods that embed previously established limits into a computer code are coupled with parametric three-dimensional Computer Aided Design (CAD) models that have associated two-dimensional drawing packs. The result are design methods that engineers can use to deliver a full set of manufacturing drawings for a centrifugal industrial fan with less than one man week of engineering time required for each design.A historic focus on the automation and control of industrial centrifugal fan order related engineering and production of manufacturing drawings has helped to ensure that in-service failures are rare; however, this ultimately limits the resultant design's aerodynamic efficiency. Scaling and interpolating fan performance using methods developed in the 1950's [2,3] results in reliably predicting aerodynamic performance, but inevitably also sub-optimal performance.

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Predicting Blade Leading Edge Erosion in an Axial Induced Draft Fan

Cited by 30 publications

References 21 publications

Machine learnt prediction method for rain erosion damage on wind turbine blades

Machine learnt prediction method for rain erosion damage on wind turbine blades

Simulation of Volcanic Ash Ingestion Into a Large Aero Engine: Particle–Fan Interactions

Predicting the Performance of an Industrial Centrifugal Fan Incorporating Cambered Plate Impeller Blades

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