Rolls-Royce Deutschland

Dimensionless frequency scaling laws for active separation control on flat-plate wings, using dielectric barrier discharge plasma actuators, were examined on the basis of maximum increases in the lift coefficient and compared with hovering insect wing-flapping frequencies. Data for a range of angles of attack (15-35 degrees), Reynolds numbers (3 & times;103 to 20 & times;103), and semispan wing aspect ratios (0.75-infinity) collapsed best when scaled with the streamwise-projected chord height. The "forcing Strouhal number" that produced the largest lift coefficient increments (mean +/- standard deviation=0.267 +/- 0.033) was linked to the conventional bluff-body Strouhal number by recognizing that drag and lift on flat-plate wings are directly proportional at a fixed angle of attack. Flowfield measurements used to determine the time-averaged separation bubble height and local velocity at separation showed that the universal Strouhal number (similar to 0.16)-developed for bluff-body and separation-bubble vortex shedding-can be further generalized to active separation control. Insect wing-flapping frequencies in hover were examined on the basis of Strouhal number scaling and corresponded to the optimum forcing range, although angle-of-attack estimates were a source of uncertainty. For flapping wings, the universal Strouhal number scaling can only be validated with accurate separation bubble height and separation velocity measurements.

A Bayesian framework is proposed for building and calibrating physics-based models of industrial thermoacoustic systems, using the Rolls-Royce SCARLET test rig as a case study. Several candidate models are constructed, and their uncertain parameters are inferred directly from experimental data. Bayesian model comparison is then used to identify the most probable candidate model, balancing data fit and model complexity. The selected physics-based model reproduces both non-reacting and reacting measurements with high precision. This model is then used to confirm the findings of recent work, which demonstrates an inconsistency in a commonly used method for measuring the flame response in complex combustion chambers. This paper goes further to provide an improved method for identifying the flame response from data. Moreover, because this process learns the parameters of a flame model rather than just processing the experimental data, the model can interpolate and extrapolate, and provide deeper insight into the underlying physics.

Predicting how the thermoacoustic response of a combustor changes across operating conditions is a long-standing challenge because small uncertainties in the flame response lead to large uncertainties in the thermoacoustic response. In this paper, we address this challenge for the Rolls-Royce SCARLET test rig using a combination of Bayesian inference, Gaussian process regression, and information-theoretic experiment design. Starting from a physics-based acoustic network model whose flame parameters are inferred at several operating conditions using Bayesian inference (described in a companion paper), we use Gaussian process regression in Part A to learn how the five parameters of a flame model vary with six operating condition parameters. The resulting Gaussian process model predicts the flame response at unseen conditions with quantified uncertainty, and, when coupled into the acoustic network, produces operating maps of the full thermoacoustic response. In Part B, we use metrics from information theory to identify the small number of forcing frequencies that are most informative about the flame parameters. For the SCARLET rig, three optimally chosen frequencies recover the flame transfer function to the same fidelity as the full dataset of around 20 frequencies forced from both upstream and downstream, reducing the data required by up to 90%.

Abstract Predictive models for aircraft engines are being developed to forecast engine health conditions based on available operational data. In-service engine data will be the most valuable information to build such a model but may not always be accessible or sufficient. A pre-trained model can be a possible alternative, if it can be extended to new and other engine families. The objective of this paper is to demonstrate knowledge transfer by evaluating a predictive engine health model across different applications of engines. A predictive engine health model was developed using Machine Learning techniques applied to engine health monitoring (EHM) data gathered from the fleets of three different turbofan engines. Each model was built exclusively by training on the EHM data from each fleet of turbofan engines. The pre-trained models made predictions for their source (baseline case), and for other fleets of turbofan engines (transfer knowledge cases). The results show that the transfer knowledge cases have mean root-mean-square-error (RMSE) values, between 5 °C and 8 °C, which is about 2 times higher than the baseline case. The baseline case, represented by a smaller interquartile range (IQR), between 0.4 °C and 1.6 °C, have less variation in the prediction results than that of the transfer knowledge cases which have IQR values ranging from 1.6 °C to 6 °C. Such results indicate a generative predictive engine health framework may be developed with the capacity to be scaled across multiple gas turbine engine classes and applications.

To model and predict thermoacoustic instabilities of a combustion system it is common practice that a flame transfer function (FTF) describes the flame dynamics. Therefore, Rolls-Royce developed a high-pressure single sector rig (SCARLET) to acoustically measure flame-transfer matrices (FTM) at realistic engine-operation conditions along the whole flight cycle. From the FTM measurement the FTF can be extracted for modelling purposes, by taking only the two-two-element of the matrix into account — that element describes the transfer of velocity fluctuations before the flame into velocity fluctuations after the flame front. As part of our net zero initiative to decarbonise future aviation, Rolls-Royce is developing hydrogen technologies. As part of the development of a new and interchangeable hydrogen fuel injector for an existing combustion system environment (a demonstrator engine), measurements of flame transfer matrices have been performed in the SCARLET rig to de-risk the occurrence of thermoacoustic instabilities. These measurements allow a direct comparison of transfer matrices of kerosene and hydrogen flames within the same combustor environment. The study allows a direct assessment between gaseous hydrogen and liquid kerosene fuel preparation to see how sensitive the FTF is to the fuel changes. Additionally, the direct influence on gains and time delays for selected operation conditions will be provided.