Applications of deep learning to physical simulations such as Computational Fluid Dynamics have recently experienced a surge in interest, and their viability has been demonstrated in different domains. However, due to the highly complex, turbulent, and three-dimensional flows, they have not yet been proven usable for turbomachinery applications. Multistage axial compressors for gas turbine applications represent a remarkably challenging case, due to the high-dimensionality of the regression of the flow field from geometrical and operational variables. This paper demonstrates the development and application of a deep learning framework for predictions of the flow field and aerodynamic performance of multistage axial compressors. A physics-based dimensionality reduction approach unlocks the potential for flow-field predictions, as it re-formulates the regression problem from an unstructured to a structured one, as well as reducing the number of degrees of freedom. Compared to traditional “black-box” surrogate models, it provides explainability to the predictions of the overall performance by identifying the corresponding aerodynamic drivers. The model is applied to manufacturing and build variations, as the associated performance scatter is known to have a significant impact on $ \mathrm{C}{\mathrm{O}}_2 $ emissions, which poses a challenge of great industrial and environmental relevance. The proposed architecture is proven to achieve an accuracy comparable to that of the CFD benchmark, in real-time, for an industrially relevant application. The deployed model is readily integrated within the manufacturing and build process of gas turbines, thus providing the opportunity to analytically assess the impact on performance with actionable and explainable data.

Effusion cooling is the state-of-the-art cooling technology for gas turbine hot-gas path components. Typically, effusion cooling holes across the entire combustor liner are aligned with the combustor axis, rendering a nominal zero compound angle between highly directional miniature effusion cooling jets and the main flow direction. The pitch of effusion cooling holes is optimised accordingly. However, the swirling main flow results in a non-zero compound angle and an effectively different pitch from the design. The directional effect of effusion cooling as a result of swirling main flow on the adiabatic film cooling effectiveness (AFE) is a combined effect of a non-zero compound angle and a varied pitch. The current experimental study aims to investigate the isolated effects of compound angle on AFE by excluding the influences of varying pitch. With an improved understanding of the sole effects of non-zero compound angles on AFE, the roles that a varied pitch plays in modifying AFE are further discussed to guide future effusion cooling designs under swirling main flow conditions. Binary pressure sensitive paint (PSP) was used to determine AFE experimentally.

This paper proposes a methodology to define and quantify the precision uncertainties in aerothermodynamic cycle model comparisons. The total uncertainty depends on biases and random errors commonly found in such comparisons. These biases and random errors are classified and discussed based on observations found in the literature. The biases account for effects such as differences in model inputs, the configurations being simulated, and thermodynamic packages. Random errors consider the effects on the physics modeling and numerical methods used in cycle models. The methodology is applied to a comparison of two cycle models, designated as the model subject to comparison and reference model, respectively. The former is the so-called Aerothermodynamic Generic Cycle Model developed in-house at the Laboratory of Applied Research in Active Control, Avionics and AeroServoElasticity (LARCASE); the latter is an equivalent model programmed in the Numerical Propulsion System Simulation (NPSS). The proposed methodology is intended to quantify the bias and random errors effects on different cycle parameters of interest, such as thrust, specific fuel consumption, among others. Each bias and random errors are determined by deliberately preventing the effects from other biases and random errors. The methodology presented in this paper can be extended to other cycle model comparisons. Moreover, the uncertainty figures derived in this work are recommended to be used in other model comparisons when no better reference is available.

To investigate the downstream rim seal gas ingestion characteristics of a 1.5-stage turbine, the URANS equations were solved numerically using the SST turbulence model. The effects of different purge flow rates and the second vane on the ingestion characteristics of the aft cavity and the nonuniform fluctuations of the main gas path pressure are analysed. The results showed that the aft cavity is affected by the combined effects of the blade and the second vane, and the potential field at the leading edge of the second vane greatly influence the airflow variation in the aft cavity, which enhances the ingress of the mainstream into the wheel-space. The front purge flow weakens the egress between the suction side of the blade and the suction side of the second vane. The potential field at the leading edge of the second vane suppresses the nonuniform distribution of airflow in the aft cavity caused by the rotational effect of the blade.

This paper proposes an alternating elliptical impingement chamber in the leading edge of a gas turbine to restrain the cross flow and enhance the heat transfer, and investigates the detailed flow and heat transfer characteristics. The chamber consists of straight sections and transition sections. Numerical simulations are performed by solving the three-dimensional (3D) steady Reynolds-Averaged Navier–Stokes (RANS) equations with the Shear Stress Transport (SST) k– $\omega$ turbulence model. The influences of alternating the cross section on the impingement flow and heat transfer of the chamber are studied by comparison with a smooth semi-elliptical impingement chamber at a cross-flow Velocity Ratio (VR) of 0.2 and Temperature Ratio (TR) of 1.00 in the primary study. Then, the effects of the cross-flow VR and TR are further investigated. The results reveal that, in the semi-elliptical impingement chamber, the impingement jet is deflected by the cross flow and the heat transfer performance is degraded. However, in the alternating elliptical chamber, the cross flow is transformed to a pair of longitudinal vortices, and the flow direction at the centre of the cross section is parallel to the impingement jet, thus improving the jet penetration ability and enhancing the impingement heat transfer. In addition, the heat transfer in the semi-elliptical chamber degrades rapidly away from the stagnation region, while the longitudinal vortices enhance the heat transfer further, making the heat transfer coefficient distribution more uniform. The Nusselt number decreases with increase of VR and TR for both the semi-elliptical chamber and the alternating elliptical chamber. The alternating elliptical chamber enhances the heat transfer and moves the stagnation point up for all VR and TR, and the heat transfer enhancement is more obvious at high cross-flow velocity ratio.

The objective of this work is to computationally investigate the impact of an incidence-tolerant rotor blade concept on gas turbine engine performance under off-design conditions. When a gas turbine operates at an off-design condition such as hover flight or takeoff, large-scale flow separation can occur around turbine blades, which causes performance degradation, excessive noise, and critical loss of operability. To alleviate this shortcoming, a novel concept which articulates the rotating turbine blades simultaneous with the stator vanes is explored. We use a finite-element-based moving-domain computational fluid dynamics (CFD) framework to model a single high-pressure turbine stage. The rotor speeds investigated range from 100% down to 50% of the designed condition of 44,700 rpm. This study explores the limits of rotor blade articulation angles and determines the maximal performance benefits in terms of turbine output power and adiabatic efficiency. The results show articulating rotor blades can achieve an efficiency gain of 10% at off-design conditions thereby providing critical leap-ahead design capabilities for the U.S. Army Future Vertical Lift (FVL) program.

This paper presents a novel method for quantifying the effect of ambient, environmental and operating conditions on the progression of degradation in aircraft gas turbines based on the measured engine and environmental parameters. The proposed equivalent operating time (EOT) model considers the degradation modes of fouling, erosion, and blade-tip wear due to creep strain, and expresses the actual degradation rate over the engine clock time relative to a pre-defined reference condition. In this work, the effects of changing environmental and engine operating conditions on the EOT for the core engine booster compressor and the high-pressure turbine were assessed by performance simulation with an engine model. The application to a single and multiple flight scenarios showed that, compared to the actual engine clock time, the EOT provides a clear description of component degradation, prediction of remaining useful life, and sufficient margin for maintenance action to be planned and performed before functional failure.

At the preliminary design phase of a gas turbine, time is crucial in capturing new business opportunities. In order to minimise the design time, the concept of Preliminary Multi-Disciplinary Optimisation (PMDO) was used to create parametric models, geometry and cooling flow correlations towards a new design process for turbine housing and shroud segments. First, dedicated parametric models were created because of their reusability and versatility. Their ease of use compared to non-parameterised models allows more design iterations and reduces set-up and design time. A user interface was developed to interact with the parametric models and improve the design time. Second, geometry correlations were created to minimise the number of parameters used in turbine housing and shroud segment design. Third, a correlation study was conducted to minimise the number of engine parameters required in cooling flow predictions. The parametric models, the geometry correlations, and the user interface resulted in a time saving of 50% and an increase in accuracy of 56% compared to the existing design system. For the cooling flow correlations, the number of engine parameters was reduced by a factor of 6 to create a simplified prediction model and hence a faster shroud segment selection process.

Gas path diagnostics is one of the most effective condition monitoring techniques in supporting condition-based maintenance of gas turbines and improving availability and reducing maintenance costs of the engines. The techniques can be applied to the health monitoring of different gas path components and also gas path measurement sensors. One of the most important measurement sensors is that for the engine control, also called the power setting sensor, which is used by the engine control system to control the operation of gas turbine engines. In most of the published research so far, it is rarely mentioned that faults in such sensors have been tackled in either engine control or condition monitoring. The reality is that if such a sensor degrades and has a noticeable bias, it will result in a shift in engine operating condition and misleading diagnostic results.

In this paper, the phenomenon of a power-setting sensor fault has been discussed and a gas path diagnostic method based on a Genetic Algorithm (GA) has been proposed for the detection of power-setting sensor fault with and without the existence of engine component degradation and other gas path sensor faults. The developed method has been applied to the diagnostic analysis of a model aero turbofan engine in several case studies. The results show that the GA-based diagnostic method is able to detect and quantify the power-setting sensor fault effectively with the existence of single engine component degradation and single gas path sensor fault. An exceptional situation is that the power-setting sensor fault may not be distinguished from a component fault if both faults have the same fault signature. In addition, the measurement noise has small impact on prediction accuracy. As the GA-based method is computationally slow, it is only recommended for off-line applications. The introduced GA-based diagnostic method is generic so it can be applied to different gas turbine engines.

The semi-analytical prediction of pollutants emissions from gas turbines in the conceptual design phase is addressed in this paper. The necessity of this work arose from an urgent need for a comprehensive model that can quickly provide data in the conceptual design phase. Based on the available inputs data in the initial phases of the design process, a chemical reactor network (CRN) is defined to model the combustion with a detailed chemistry. In this way, three different chemical mechanisms are studied for Jet-A aviation fuel. Furthermore, the droplet evaporation for liquid fuel and the non-uniformity in fuel-air mixture are modelled. The results of a developed augmented modelling tool are compared with the pollutants data of two annular engine's combustors. The CRN results have good agreement with the actual engine test rig emissions output. In conclusion, the augmented CRN has shown to be efficient in predicting engine emissions with a very short executing time (few seconds) using a small CPU requirement such as a personal computer.

The main aims of this research are, at first, combustion instability study based on equivalence ratio oscillation, and, secondly, investigation various frequency modes of combustion instability, taking combustion chamber geometry into account. Considering the configuration of the simulated combustion chamber, excitation probability of the longitudinal modes is higher than that of transversal modes. The reason of this fact is that the resonance frequency values of the longitudinal modes are less than those of transversal modes. So, the most important frequency mode, during combustion instability, is the first longitudinal mode. In this paper thermo-acoustic instability model is utilized for pre-mixed gas turbines combustion chamber, founded on equivalence ratio oscillation. For this purpose Lieuwen method is developed in order to attain the phase difference between pressure and heat release oscillations. Results concluded from combustion instability simulation for the first longitudinal mode, considering its importance, are compared with experimental data and good agreement is observed.