Impact of the Precessing Vortex Core on NOx Emissions in Premixed Swirl-Stabilized Flames: An Experimental Study

The reduction of polluting NOx emission remains a driving factor in the design process of swirl-stabilized combustion systems, to meet strict legislative restrictions. In reacting swirl flows, hydrodynamic coherent structures, such as periodic large-scale vortices in the shear layer, induce zones with increased heat release rate fluctuations in connection with temperature peaks, which lead to an increase of NOx emissions. Such large-scale vortices can be induced by the helical coherent structure known as precessing vortex core (PVC), which influences the flow and flame dynamics of reacting swirl flows under certain operating conditions. We developed an active flow control system, which allows for a targeted actuation of the PVC, to investigate its impact on important combustion properties. In this study, the direct active flow control is used to actuate a PVC of arbitrary frequency and amplitude, which facilitates a systematic study of the impact of the PVC on NOx emissions. In the course of the present work, a perfectly premixed flame, which slightly damps the PVC, is studied in detail. Since the PVC is slightly damped, it can be precisely excited by means of open-loop flow control. In connection with time-resolved OH*-chemiluminescence and stereoscopic PIV measurements, the flame and flow response to PVC actuation as well as the impact of the actuated PVC on flow and flame dynamics are characterized. It turns out that the PVC rolls up the inner shear layer, which results in an interaction of PVC-induced vortices and flame. This interaction considerably influences the measured level of NOx emissions, which grow with increasing PVC amplitude in a perfectly premixed flame. Nearly the same increase is to be seen for a partially premixed flame. This in contrast to previous studies, where the PVC is assumed to reduce the NOx emissions due to vortex-enhanced mixing.

Reliable Calculation of Thermoacoustic Instability Risk Using an Imperfect Surrogate Model

One of the fundamental tasks in performing robust thermoacoustic design of gas turbine combustors is calculating the modal instability risk, i.e., the probability that a thermoacoustic mode is unstable, given various sources of uncertainty (e.g., operation or boundary conditions). To alleviate the high computational cost associated with conventional Monte Carlo simulation, surrogate modeling techniques are usually employed. Unfortunately, in practice it is not uncommon that only a small number of training samples can be afforded for surrogate model training. As a result, epistemic uncertainty may be introduced by such an “inaccurate” model, provoking a variation of modal instability risk calculation. In the current study, using Gaussian Process (GP) as the surrogate model, we address the following two questions: Firstly, how to quantify the variation of modal instability risk induced by the epistemic surrogate model uncertainty? Secondly, how to reduce the variation of risk calculation given a limited computational budget for the surrogate model training? For the first question, we leverage on the Bayesian characteristic of the GP model and perform correlated sampling of the GP predictions at different inputs to quantify the uncertainty of risk calculation. We show how this uncertainty shrinks when more training samples are available. For the second question, we adopt an active learning strategy to intelligently allocate training samples, such that the trained GP model is highly accurate particularly in the vicinity of the zero growth rate contour. As a result, a more accurate and robust modal instability risk calculation is obtained without increasing the computational cost of surrogate model training.

Detailed Study of Front Module for Low-Emission Combustor

At the ASME Turbo Expo 2018 conference held in Oslo (Norway) on the 11th-15th of June 2018, the paper GT2018-75419 «Experience of Low-Emission Combustion of Aviation and Bio Fuels in Individual Flames after Front Mini-Modules of a Combustion Chamber» was published. This paper continues the studies devoted to the low-emission combustion of liquid fuels in GTE combustors. The paper presents a description of more detailed studies of the front module with a staged pneumatic fuel spray. The aerodynamic computations of the front module were conducted, and the disperse characteristics of the fuel-air spray were measured. The experimental research was carried out in two directions: 1) probing of the 3-burner sector flame tube at the distance of one third of its length (temperature field and gas sampling); 2) numerical study of the model combustor with actual arrangement of the modules in the dome within a wide range of fuel-air ratio. The calculated and experimental data of velocity field behind the front module were compared. And new data about the flame structure inside the test sector were obtained. Experimental data confirm the results of preliminary studies of the 3-burner sector: combustion efficiency is higher than 99.8%, EiNOx is at the level of 2–3 g/fuel kg at the combustor inlet air temperature of 680K and fuel-air ratio of 0.0225. The conducted research allowed to receive additional information on the influence of some design units on the pollutant emission and to estimate the different elements of computational methods for simulation of a low-emission combustor with a multi-atomizer dome.

Sequential Combustion in Ansaldo Energia Gas Turbines: The Technology Enabler for CO2-Free, Highly Efficient Power Production Based on Hydrogen

Today gas turbines and combined cycle power plants play an important role in power generation and in the light of increasing energy demand, their role is expected to grow alongside renewables. In addition, the volatility of renewables in generating and dispatching power entails a new focus on electricity security. This reinforces the importance of gas turbines in guaranteeing grid reliability by compensating for the intermittency of renewables. In order to achieve the Paris Agreement’s goals, power generation must be decarbonized. This is where hydrogen produced from renewables or with CCS (Carbon Capture and Storage) comes into play, allowing totally CO2-free combustion. Hydrogen features the unique capability to store energy for medium to long storage cycles and hence could be used to alleviate seasonal variations of renewable power generation. The importance of hydrogen for future power generation is expected to increase due to several factors: the push for CO2-free energy production is calling for various options, all resulting in the necessity of a broader fuel flexibility, in particular accommodating hydrogen as a future fuel feeding gas turbines and combined cycle power plants. Hydrogen from methane reforming is pursued, with particular interest within energy scenarios linked with carbon capture and storage, while the increased share of renewables requires the storage of energy for which hydrogen is the best candidate. Compared to natural gas the main challenge of hydrogen combustion is its increased reactivity resulting in a decrease of engine performance for conventional premix combustion systems. The sequential combustion technology used within Ansaldo Energia’s GT36 and GT26 gas turbines provides for extra freedom in optimizing the operation concept. This sequential combustion technology enables low emission combustion at high temperatures with particularly high fuel flexibility thanks to the complementarity between its first stage, stabilized by flame propagation and its second (sequential) stage, stabilized by auto-ignition. With this concept, gas turbines are envisaged to be able to provide reliable, dispatchable, CO2-free electric power. In this paper, an overview of hydrogen production (grey, blue, and green hydrogen), transport and storage are presented targeting a CO2-free energy system based on gas turbines. A detailed description of the test infrastructure, handling of highly reactive fuels is given with specific aspects of the large amounts of hydrogen used for the full engine pressure tests. Based on the results discussed at last year’s Turbo Expo (Bothien et al. GT2019-90798), further high pressure test results are reported, demonstrating how sequential combustion with novel operational concepts is able to achieve the lowest emissions, highest fuel and operational flexibility, for very high combustor exit temperatures (H-class) with unprecedented hydrogen contents.

Evaluation of the Uniform Conditional State Method for Turbulence-Chemistry Interaction Modelling of Swirl-Stabilized Flames

This paper considers a variation on Conditional Moment Closure (CMC) modelling for turbulence-chemistry interaction called the Uniform Conditional State (UCS) model and its application to the prediction of swirl-stabilized flames. UCS is essentially a zero-spatial dimensional, multi-condition CMC method. Unlike conventional CMC methods, for flames that are in (statistically) steady flows, the chemistry can be solved a priori in conditional space only. The reactive scalars are then mapped into real space by taking the inner product of the resulting conditional averages with the joint probability density function of the conditioning variables, here taken to have a presumed form that is a function of the mean and variance of the conditioning variables. Two conditioning variables are used, mixture fraction and progress variable. The combination of these allows for the resulting chemistry table to be applicable to both premixed and non-premixed combustion but also in the partially-premixed regime. In doing so, this new approach is promising to be highly suitable for simulating industrial applications and complex geometries. Another promising aspect is the universal applicability to different fuels and kinetic mechanisms providing great flexibility to the user of this method. Ultimately it is intended to aid the development of industrial burners by providing detailed information about the local composition and emission production, while keeping computational costs significantly low. Not only does this provide additional insight into global emissions and fuel consumption of a new design, but it allows for variability between different stages of mixedness as well as the testing of, for example, alternative fuels in established burner configurations. In this present study a comparison of different fuels and initial conditions is being conducted to analyze their effect on the resulting UCS solution — meaning the chemical source-terms, composition and thermodynamic state in conditional space. Furthermore the use of the UCS solutions as a predictive method in a RANS simulation is being presented here. The paper illustrates the UCS predictions and compares them to experimental data, as well as previously published simulation results of more established modelling approaches. The experimental test case chosen is a model combustor with a swirl-stabilized flame and high technical relevance which demonstrates the applicability of the UCS method to industrial designs for aero engines. Further investigations have begun including the application of this new tool to a real industrial combustor within the framework of this collaboration with MTU Aero Engines AG.

Boundary Layer Flashback Model for Hydrogen Flames in Confined Geometries Including the Effect of Adverse Pressure Gradient

The combustion properties of hydrogen make premixed hydrogen-air flames very prone to boundary layer flashback. This paper describes the improvement and extension of a boundary layer flashback model from Hoferichter [1] for flames confined in burner ducts. The original model did not perform well at higher preheat temperatures and overpredicted the backpressure of the flame at flashback by 4–5x. By simplifying the Lewis number dependent flame speed computation and by applying a generalized version of Stratford’s flow separation criterion [2], the prediction accuracy is improved significantly. The effect of adverse pressure gradient flow on the flashback limits in 2° and 4° diffusers is also captured adequately by coupling the model to flow simulations and taking into account the increased flow separation tendency in diffuser flow. Future research will focus on further experimental validation and direct numerical simulations to gain better insight into the role of the quenching distance and turbulence statistics.

Effect of Fuel Stage Proportion on Flame Position in an Internally-Staged Combustor

To study the effect of fuel stage proportion on flame position and combustion characteristics of the internally-staged combustor, a detailed numerical investigation is performed in the present paper. The prediction method of flame position is established by analyzing the variations of the distribution of intermediate components and the turbulent flame speed. Meanwhile, the flame position is simulated to verify the accuracy of the prediction method. It is demonstrated that the flame position prediction model established in this paper can accurately predict the flame position under different fuel stage proportions. On this basis, special attention is paid to analyze the variation of velocity field, temperature field, distribution of intermediate components and emissions under different fuel stage proportions. As the proportion of pilot fuel stage increases slightly, the mass fraction of fuel at the combustor dome increases. In addition, the combustion characteristics change significantly with the increase in the proportion of pilot stage fuels. The flame moves downstream and the high temperature area increases as the proportion of pilot fuel increases. In particular, when the proportion of pilot stage reaches 3%, the highest flame temperature is generated due to the most concentrated reaction area, resulting in the largest emission of NOx. At the same time, due to the most complete reaction, the minimum CO emission is produced. When the proportion of pilot fuel stage reaches 1%, the NOx emission is the lowest, and the highest CO emission is generated due to the incomplete reaction.

Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise

Experiments are performed on a turbulent swirling flame placed inside a vertical tube whose fundamental acoustic mode becomes unstable at higher powers and equivalence ratios. The power, equivalence ratio, fuel composition and boundary condition of this tube are varied and, at each operating point, the combustion noise is recorded. In addition, short acoustic pulses at the fundamental frequency are supplied to the tube with a loudspeaker and the decay rates of subsequent acoustic oscillations are measured. This quantifies the linear stability of the system at every operating point. Using this data for training, we show that it is possible for a Bayesian ensemble of neural networks to predict the decay rate from a 300 millisecond sample of the (un-pulsed) combustion noise and therefore forecast impending thermoacoustic instabilities. We also show that it is possible to recover the equivalence ratio and power of the flame from these noise snippets, confirming our hypothesis that combustion noise indeed provides a fingerprint of the combustor’s internal state. Furthermore, the Bayesian nature of our algorithm enables principled estimates of uncertainty in our predictions, a reassuring feature that prevents it from making overconfident extrapolations. We use the techniques of permutation importance and integrated gradients to understand which features in the combustion noise spectra are crucial for accurate predictions and how they might influence the prediction. This study serves as a first step towards establishing interpretable and Bayesian machine learning techniques as tools to discover informative relationships in combustor data and thereby build trustworthy, robust and reliable combustion diagnostics.

Influence of Humidity and Fuel Hydrogen Content on Ultrafine Non-Volatile Particulate Matter Formation in RQL Gas Turbine Technology

To address the known Local Air Quality impacts of ultrafine combustion derived soot, the International Civil Aviation Organisation (ICAO) have recently adopted a non-volatile Particulate Matter (nvPM) regulation in addition to those of NOx, UHC’s and CO for civil aviation gas turbines. Increased water humidity is known to reduce the formation of NOx in flames through localised temperature reduction, however its impact on emitted nvPM is to date not clearly understood. To address this knowledge gap, nvPM formation mechanisms were assessed empirically at increasing water loadings both at atmospheric pressure — in a RQL representative optical combustor fuelled with Jet A and alternative fuel blends — and during a full-scale Rolls-Royce aero-derivative Gas Turbine test fuelled on Diesel. In line with previous studies, in the RQL combustor rig it was observed that increased hydrogen content in the test fuel — associated with a 100% Gas-To-Liquid (GTL) derived aviation kerosene with low aromatic content (0.05%) — reduced nvPM number concentrations by an order of magnitude compared to a baseline Jet A-1 fuel with representative aromatic content (24.24%). For all fuels tested it was also observed that an elevated water loading in the primary combustion zone (≤ 0.05 kg /kg of dry air), representative of maximum global humidity levels, resulted in reductions of both nvPM number and mass concentrations of 40% and 60% respectively. During a full-scale Rolls-Royce gas turbine study similar trends were observed, with an 85% reduction in measured nvPM mass whilst water was injected into the combustor at flow rates 25% higher than the diesel fuel flow. The nvPM reductions in both experiments are significantly larger than can be explained by water dilution effects alone, with less impact noted for fuels with higher hydrogen content. This suggests the reduction may be in part due to chemistry. Preliminary chemical kinetic investigations were undertaken using CHEMKIN-PRO and suggest that the soot reduction mechanism is potentially via a reduction in PAH formation within the flame zone. However, further analysis is required to validate if this mechanism is dominated by in-flame OH reduction mechanisms or influenced significantly by other factors associated with water dilution and reduced flame temperatures.

Assimilation of Experimental Data to Create a Quantitatively-Accurate Reduced Order Thermoacoustic Model

We combine a thermoacoustic experiment with a thermoacoustic reduced order model using Bayesian inference to accurately learn the parameters of the model, rendering it predictive. The experiment is a vertical Rijke tube containing an electric heater. The heater drives a base flow via natural convection, and thermoacoustic oscillations via velocity-driven heat release fluctuations. The decay rates and frequencies of these oscillations are measured every few seconds by acoustically forcing the system via a loudspeaker placed at the bottom of the tube. More than 320,000 temperature measurements are used to compute state and parameters of the base flow model using the Ensemble Kalman Filter. A wave-based network model is then used to describe the acoustics inside the tube. We balance momentum and energy at the boundary between two adjacent elements, and model the viscous and thermal dissipation mechanisms in the boundary layer and at the heater and thermocouple locations. Finally, we tune the parameters of two different thermoacoustic models on an experimental dataset that comprises more than 40,000 experiments. This study shows that, with thorough Bayesian inference, a qualitative model can become quantitatively accurate, without overfitting, as long as it contains the most influencial physical phenomena.