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Solid particle ingestion is one of the main causes of gas turbine compressors degradation for both heavy-duty and aero-propulsion applications. Particles that enter the engine can stick to the internal surfaces and then form deposits. These, in turn, involve a change in the surface roughness and then performance deterioration. In the literature, several strategies have been proposed in order to account for the deposits effects on compressor numerical simulations. Among the others, the most used is the mesh-morphing strategy, which consists of the modification of the computational grid according to the particle deposition. Even though it is well suited for turbine fouling, the large computational costs and complexity of this strategy are not acceptable for compressor simulations. In this work, an innovative microscale-based approach has been proposed. An axial compressor deposition experiment from the literature has been considered as the reference case to test the reliability of the presented strategy. The main advantage of this approach consists of reducing both computational costs and numerical instability since it does not need the modification of the mesh. The deposition has been modelled by means of the OSU model and the roughness influence on the fluid flow has been accounted for by using the well-known sand-grain-roughness height (ks). Efforts have been made to find the ks correlation that best represents a fouled surface. The presented strategy enables the prediction of the fouling effects on axial flow compressors, and the prediction of the performance losses during the operation.

Numerical simulations of particle-laden flows have received growing attention in the last decade, due to the broad spectrum of industrial applications in which discrete phases prediction is of interest. Among these, ingestion of particles by turbomachinery is an area that is seeing vivid research and studies. The most common technique to tackle this kind of problem is the Eulerian-Lagrangian method, in which individual particles are tracked inside the domain. At the same time, in multi-stage turbomachinery simulations interfaces are needed to couple the flow solution in adjacent domains in relative motion. In this work, an open-source extension for Lagrangian simulations in multistage rotating machines is presented in the foam-extend environment. Firstly, a thorough discussion of the implementation is presented, with particular emphasis on particle passage through General Grid Interfaces (GGI) and mixing planes. Moreover, a mass-conservative particle redistribution technique is described, as such a property is requested at interfaces between Multiple Reference Frame (MRF). The peculiarities of the algorithm are then shown on a relevant test-case. Eventually, three turbomachinery applications are presented, with growing complexity, to show the capabilities of the numerical code in real-life applications. Simulation results in terms of erosion and impacts on aerodynamic surfaces are also presented as examples of possible parameters of interest in particle-laden flow computations.

Atmospheric particulate is one of the main causes of performance degradation in gas turbine engines, especially in the aeronautical field where filter barriers are absent. The ingested particles can stick to the blade surfaces of the engine, varying their shape and roughness. As a consequence, engine performance degradation takes place. The type and the amount of the particles ingested depend on the flight zones and altitude. During their missions, aircrafts follow a prescribed path defined in terms of altitude, longitude, and latitude. During its route, the aircraft engine encounters different environments characterized by different temperature, pressure, and air composition. Regarding the latter issue, the knowledge of this characteristic can be key information when these statistics are needed for obtaining data useful for engine degradation assessment or prediction. Many satellites, such as the environmental satellite CALIPSO, are employed to study the terrestrial aerosol and clouds profile by using a LIDAR (Laser Detection and Ranging). This technology is commonly used to determine the distance between a light emitter and an object and it is based on the light refraction phenomenon. Backscatter coefficients profiles data, which characterize the distribution of particles and aerosols in the atmosphere, are available in the open literature from the findings of CALIPSO. In this work, a new methodology to estimate the aerosol type and concentration encountered by an aircraft during a mission is proposed. To test the feasibility of this method, two aircraft missions for different length scales (medium and long haul) are analyzed and an estimate of the particulate encountered by the engines is provided. The mission analysis has been conducted by discretizing the altitude profile, longitude, and latitude coordinates of each flight and then cross-referencing them with the particulate concentration obtained from CALIPSO data.

Computational fluid dynamics (CFD) is increasingly used during the design phase of turbomachinery. Reducing the cost of such computations is one of the major challenges in the industrial field. The multi-physics phenomena and the multidisciplinary interactions needed for the design of the engine components are difficult to be faced with the classical design methods. In addition, the interest of manufacturers and operators of turbomachines in the increase in performance when degradation processes occur is growing. In the aeronautical sector degradation is among the most critical issues, as it can lead to the in-flight shutdown of the engine. In a bid to tackle these big problems, new design methods based on approximation techniques have been developed. These techniques are called surrogate models and are currently the most used design methods for the aerodynamic design of aircraft engines. In this work, the assessment of a surrogate surface built for the purpose of optimizing an HPT vane in degrading conditions is performed. Using machine learning and statistical techniques, a sensitivity analysis is conducted in order to reduce the problem dimensions. The results of the sensitivity analysis are used for a study of the surrogate, with the purpose of obtaining design guidelines when deterioration effects are considered in the design phase. The main outcome of this study is a map, that outlines the best design zone defined by the combination of the most influential parameters.

Rotor-stator interaction in turbomachinery is one of the most challenging fields in Computational Fluid Dynamics (CFD) and, in this regard, several studies can be found in the literature, concerning unsteady coupling of successive blade rows. By the way, the use of mixing plane for steady multistage calculations has been common for many years and, even though this technique is at present consolidated, it still represents a challenge when one has to cope with multiphase flow problems. In recent years, the growing capabilities of open-source codes to deal with multistage simulations offer new opportunities in the research community, since direct access to implementation details is provided. Currently, only a few particle-interface interaction models are reported in the literature, hence strong limitations in particle-laden flow simulations in multistage turbomachinery arises. In order to fill up this lack of models, the authors propose a sensitivity analysis for particle-mixing plane interaction. To compare the new methodologies, efforts have been done to adapt foam-extend interface treatment to Lagrangian tracking. The component analysed in this work is the first high-pressure stage of the Energy-Efficient Engine axial turbine. The results of this sensitivity analysis is compared to high-fidelity results obtained by a transient simulation based on the sliding mesh approach. Four different averaging techniques have been proposed, based on different easiness of implementation and physical soundness, and their performance has been assessed.

Fouling represents a major problem for Gas Turbines (GTs) in both heavy-duty and aero-propulsion applications. Solid particles entering the engine can stick to the internal surfaces and form deposits. Components' lifetime and performance can dramatically vary as a consequence of this phenomenon. These effects impact the whole engine in terms of residual life, operating stability, and maintenance costs. In the High-Pressure Turbine (HPT), in particular, the high temperatures soft the particles and promote their adhesion, especially in the short term. Unfortunately, predicting the GT response to this detrimental issue is still an open problem for scientists. Furthermore, the stochastic variations of the components operating conditions increase the uncertainty of the forecasting results. In this work, a strategy to predict the effects of turbine fouling on the whole engine is proposed. A stationary Gas Path Analysis (GPA) has been performed for this scope to predict the GT health parameters. Their alteration as a consequence of fouling has been evaluated by scaling the turbine map. The scaling factor has been found by performing Computational Fluid Dynamic (CFD) simulations of a HPT nozzle with particle injection. Being its operating conditions strongly uncertain, a stochastic analysis has been conducted. The uncertainty sources considered are the circumferential hot core location and the turbulence level at the inlet. The study enables to build of confidence intervals on the GT health parameters predictions and represents a step forward towards a robust forecasting tool.

The interaction between gas turbines and airborne particles determines detrimental effects on the performance, efficiency, and reliability of the power unit. When it is possible, the interaction is reduced by the use of inlet separators and filtration systems. In an aero engine, these barriers are difficult to implement, and only bigger particles (usually greater than 10 μm) are separated from the airflow. Small units, especially those equips helicopters, are usually affected by fouling issues, especially when the aircraft is employed in harsh environments such as firefighting and rescue activities. To recover this contamination, the unit is washed after the mission by ground operations to restore the unit performance by removing the deposits. This operation occurs during a sub-idle unit operation, and the washing process has to be effective when the engine operates in this off-design condition. In this paper, the evaluation of the washing performance during a sub-idle unit operation is carried out. The compressor unit is a multistage axial compressor that equips the Allison 250-C18 engine. The washing operation was performed by water, and a sensitivity analysis is carried out to discover the capability of water droplets to remove the contaminants. The experimental analysis involves the contamination of the unit by micro-sized soot particles and a washing operation by micro-sized water droplets. These experimental results are compared to numerical simulations to discover the effects of the washing operation on a small power unit during sub-idle operating conditions. The off-design regime imposes a specific evaluation of the proper setup of the washing strategy: flow separations involve wider regions in the compressor unit, and the removal capability is strongly related to the droplet path through the stages. The results show how in the off-design washing operation, the droplet diameter has greater importance than the water flow rate for reducing the deposits over the compressor stages.

Gas turbines (GT) are often forced to operate in harsh environmental conditions. Therefore, the presence of particles in their flow-path is expected. With this regard, deposition is a problem that severely affects gas turbine operation. Components’ lifetime and performance can dramatically vary as a consequence of this phenomenon. Unfortunately, the operating conditions of the machine can vary in a wide range, and they cannot be treated as deterministic. Their stochastic variations greatly affect the forecasting of life and performance of the components. In this work, the main parameters considered affected by the uncertainty are the circumferential hot core location and the turbulence level at the inlet of the domain. A stochastic analysis is used to predict the degradation of a high-pressure-turbine (HPT) nozzle due to particulate ingestion. The GT’s component analyzed as a reference is the HPT nozzle of the Energy-Efficient Engine (E3). The uncertainty quantification technique used is the probabilistic collocation method (PCM). This work shows the impact of the operating conditions uncertainties on the performance and lifetime reduction due to deposition. Sobol indices are used to identify the most important parameter and its contribution to life. The present analysis enables to build confidence intervals on the deposit profile and on the residual creep-life of the vane.

Fouling and erosion are two problems that severely affect gas turbines. The shape of the blade, its roughness, and its structural stability can vary as a consequence of these phenomena. The outcomes of this occurrence can span from the efficiency reduction to the engine shut down according to the nature of the material ingested, to the concentration of contaminants in the air, the cleanliness of fuel and to the particular design of the machine. In this work, an axial turbine airfoil is modified according to the requirement of less sensibility to the phenomena above mentioned, utilizing an automatic optimization algorithm. An artificial neural network surrogate approach is used for searching the optimal shape, minimizing the computational cost of the entire process. The optimum design of the blade is therefore achieved, in order to reduce the effects of deposition on the performance. The methodology here proposed is fully general and it is applied to an HPT nozzle in the present analysis.

With their fast growth rate and ability to accumulate a high percentage of their weight as lipid and carbohydrate, microalgae potentially represent an ideal feedstock for the production of biodiesel and bioethanol. In addition, microalgae offer several environmental benefits, and do not compete with food production for land, fresh water, and nutrients. Therefore, the main goal of this work is to provide a quantitative, systematic and harmonized assessment of current bio-energy potential. The analysis is conducted by considering all the main steps in detail, from cultivation to biodiesel production, and by deriving an overall estimation of energy consumption for biodiesel production. Energy consumption uncertainty is also quantified and discussed. A systematic review of all the main technologies available for all the main processing steps towards the production of biodiesel from microalgae is presented, focusing on the derivation of the Net Energy Ratio (NER) of each combination of technologies, complemented by an uncertainty analysis of the data used and those obtained in the present work. A wide scatter in the data available in the literature has been identified, highlighting the need for an uncertainty analysis. If the average overall energy consumption per unit of biodiesel mass is considered, all the routes adopting a raceway pond have a lower energy consumption, but if the uncertainty on the overall energy consumption is also considered, the minimum value of the range of NER values for some of the routes adopting a photobioreactor is comparable to the NER value obtainable by using raceway ponds. Thus, the present framework proposes a harmonized and comprehensive methodology to compare and contrast technologies for the production of biodiesel from microalgae, and is applied in this paper to identify, with an appreciation of the uncertainty, the most promising combinations of technologies.