A validation of parallel TURBO, an unsteady RANS turbomachinery solver, is performed for Rotor 35. Comparisons of the rotor's operational range for computational and experimental data as well as comparisons of its spanwise performance characteristics for a single blade passage provide depth to the validation and show a very favorable agreement. Further operational and performance comparisons against experiment are used for multiple blade passage simulations. Multiple blade passage simulations are shown to demonstrate noticable gains over the single blade passage simulation in solution accuracy against experiment. Also demonstrated are the asymmetric flow features that develop at the near stall operating condition for multiple blade passages. These single and multiple blade passage simulations are presented as groundwork for future research examining the effect of periodic boundary conditions on the growth of computational stall cells within a rotor or stage configuration.

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The application of laser Doppler vibrometry to high speed rotating structures has been hampered by technical limitations. Whereas full-field three-dimensional velocity measurements can be made on stationary structures, the capability on rotating structures is limited to low speed, one-dimensional, steady state operation. This work describes the implementation of a self-tracking laser vibrometry system which overcomes many of the limitations of current techniques for vibration measurements on rotating structures. A model of the self-tracker is developed and used to predict the effects of static misalignments on the position and velocity errors. These predictions are supported by experimental results and simplified models of the self-tracker. NOTE: (02/2011) An updated copy of this ETD was added after there were patron reports of problems with the file. / Ph. D.

This research performs an experimental study of a trailing edge blowing system that can adapt to variations in flow parameters and reduce the unsteady stator-rotor interaction at all engine operating conditions. The fan rotor of a 1/14 scale turbofan propulsion simulator is subjected to spatially periodic, circumferential inlet flow distortions. The distortions are generated by four struts that support a centerbody in the inlet mounted onto the simulator. To reduce the unsteady effects of the strut wakes on the rotor blades, the wake is re-energized by injecting mass from the trailing edge of the strut. Each strut is provided with discrete blowing holes that open out through the strut trailing edge. Each blowing hole is connected to a MEMS based microvalve, which controls the blowing rate of the hole. The microvalve is actuated by a signal voltage, generated by a PID controller that accepts free stream and wake axial flow velocities as inputs and minimizes their difference. To quantify the effectiveness of trailing edge blowing the far-field noise is measured in an anechoic chamber. The experiments are performed for two simulator test speeds, 29,500 rpm and 40,000 rpm, with and without trailing edge blowing. The maximum reduction recorded at 29,500 rpm is 8.2 dB, and at 40,000 rpm is 7.3 dB. Reductions of 2.9 dB and greater are observed at the first five harmonics of the blade passing frequency. The sound power level at the blade passing frequency, calculated from measured far-field directivity, is reduced by 4.4 dB at 29,500 rpm and by 2.9 dB at 40,000 rpm. The feasibility and advantage of active control is demonstrated by the ability of the system to respond to a step change in the inlet flow velocity, and achieve optimum wake filling in approximately 8 seconds. / Master of Science

The prediction of future engine states are useful for performance evaluation and anomaly detection of jet turbines. While a variety of modeling approaches exist, many are not capable of efficiently utilizing the vast quantities of data from test experimentation in a manner that is not opaque to the operator or observer. The literature describes several approaches to interpretable modeling on various types of systems in different domains for applications such as Remaining Useful Life estimation and accident prognosis, but do not perform prediction on measured state quantities or performance. Additionally, of modeling studies that focus on jet turbines, the data is synthetic instead of experimental. In this thesis, we utilize an attention-based neural network, the Temporal Fusion Transformer, on experimental data for prediction, allowing for interpretability and insight into model dynamics. We describe a series of experiments on different configurations of the model architecture and show that through the incorporation of physical information into the system, the models produce better forecasts and confidence qualities on all outputs, with robustness to some level of failure and noise in inputs. For the TFT, we include control inputs as future covariates and evaluate modifications to the loss function to include the physics of key performance parameters of the gas turbine as residual form equations, finding that it increases model accuracy and the usefulness of interpretability results, even when model size is reduced. These key performance parameters were derived from and introduced into the dataset, with a comparison of performance on the full dataset and a reduced dataset showing increased performance on the smaller dataset. Additionally, these interpretable models are able to provide more useful insight into system dynamics, allowing for vision into time horizon attention and model-discovered variable importance. While there is further exploration in the extent of robustness and accuracy of physics-informed attention networks, we expect this approach to lead to models with reduced training time, higher accuracy, increased user confidence in prediction, and more interpretable models which will allow for future incorporation into anomaly detection algorithms or the study of dynamic systems. / Master of Science / The prediction of future engine states are useful for performance evaluation and anomaly detection of jet turbines. While a variety of modeling approaches exist, many are not capable of efficiently utilizing the vast quantities of data from test experimentation in a manner that is not opaque to the operator or observer. The literature describes several approaches to interpretable modeling on various types of systems in different domains for applications such as Remaining Useful Life estimation and accident prognosis, but do not perform prediction on measured state quantities or performance. Additionally, of modeling studies that focus on jet turbines, the data is synthetic instead of experimental. In this thesis, we utilize an attention-based neural network on experimental data for prediction, allowing for interpretability and insight into model dynamics. We describe a series of experiments on different configurations of the model architecture and show that through the incorporation of physical information into the system, the models produce better forecasts and confidence qualities. Additionally, models with interpretability are able to provide more useful insight into system dynamics. We expect this approach to lead to models with reduced training time, higher accuracy, increased user confidence in prediction, and more interpretable models which will allow for future incorporation into anomaly detection algorithms or study of dynamic systems.

As the world faces global conflict and energy crises, major efforts are underway to find sustainable engineering solutions to reduce industrial dependence on fossil fuels and minimize climate impacts from carbon emissions. Research in the combustible fuel sector is crucial to address economic reliance on cheap carbon-based fuels for increased energy capacity and reduced greenhouse gas emissions. Ammonia (NH₃) offers high energy potential and zero carbon emissions (CO and CO₂) while serving as an effective hydrogen (H₂) carrier in power and transportation applications. Turbine-combustion research on NH₃ and H₂ fuels has been conducted to identify combustion performance parameters for high-pressure, sustainable turbomachinery. Studies on NH₃ and H₂ performance capabilities have revealed sources of thermodynamic instabilities, such as uncontrolled flames or flashback, by assessing fuel laminar burning speed (LBS) with optical data. LBS is a key combustion parameter that informs turbine design engineers about combustion physiochemistry, flashback, and efficiency. State of the art literature shows that H₂ enhances the LBS of NH₃ (φ = 1.0, SL = 5.0 – 21 cm/s) for all equivalence ratios at 1 atm and 298 K. However, H₂ dilution to NH₃ results in excess N₂O and NOx emissions, which are toxic to biological systems. Thus, further efforts are needed to reduce toxic gas emissions and identify thermodynamic engineering controls to maintain stable NH₃-H₂ flames. In this work, NH₃ and H₂ mixtures were ignited at an initial temperature and pressure of 293 – 323 K and 5 – 10 atm to understand their performance properties. The LBS was calculated using a multizone, constant volume combustion model. Experimental results showed that H₂ dilution enhances the LBS of NH₃, and chemical-kinetic sensitivity analyses identified reactions facilitating this effect. Additional flame stabilization studies investigating the Lewis number of experimental mixtures revealed that helium (He) effectively mitigates thermal-diffusion, as shown by Schlieren optical measurements.

The successful transition to global clean energy is contingent upon meeting the increasing worldwide energy demand for power while simultaneously curbing greenhouse gas emissions. This study delves into the complexities of transitioning to cleaner energy sources and the challenges posed by utilizing hydrogen and hydrogen/natural gas mixtures as a potential fuel source alternative to traditional carbon-based combustion cycles. By addressing the technical intricacies and conducting thorough testing, researchers aim to enhance our understanding of auto-ignition behavior in different fuel-air mixtures under varying conditions, ultimately contributing to the development of safer and more efficient energy solutions in the pursuit of clean and sustainable power generation. This study outlines the test methodology employed to assess conditions leading to auto-ignition for various fuel-air mixtures operating at different pressures (1 - 30 atm) and temperatures. The testing encompassed 100% H2 and multiple H2/NG blends at stoichiometric conditions. Similar testing was conducted for 100% NG to validate the test procedures and data collection methods referenced in previous literature. Under atmospheric conditions, 0-1 ATM, H2 exhibits a broader flammability range of EQs where ignition is more likely to occur compared to methane. H2's flammability ranges from 4% to 75% molar (volume) fuel concentration, corresponding to an EQ range of 0.137 - 2.57, while methane's flammability limit spans from 5% to 15% molar (volume) or an EQ between 0.53 – 1.58. Previous studies have explored the effect of longer hydrocarbons present in natural gas mixtures, with ethane (C2H6) and propane (C3H8) shown to decrease the ignition temperature (AIT) of natural gas, particularly at elevated pressures. These longer hydrocarbons are inclined to promote ignition in richer conditions, whereas methane tends to ignite more readily in slightly lean conditions. Besides pressure, fuel, and EQ, numerous variables such as chamber volume size, chamber materials, presence of diluents, and other factors can influence the AIT. The results revealed that, at atmospheric pressures, an increase in H2 concentration led to a reduced AIT. However, at 30 atm, a higher presence of H2 increased the AIT. At pressures exceeding 10 atm, an increased equivalence ratio resulted in a decreased AIT for all mixtures, with NG, exhibiting the greatest sensitivity to equivalence ratio variations.