Academic literature on the topic 'Low-frequency oscillations (LFO)'
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Journal articles on the topic "Low-frequency oscillations (LFO)"
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Wang, Xiaoli, and Qi Zhou. "Application of Hilbert Spectrum based on SWT to Low-Frequency Oscillation Analysis." Journal of Physics: Conference Series 2378, no. 1 (December 1, 2022): 012032. http://dx.doi.org/10.1088/1742-6596/2378/1/012032.
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Abstract Low-frequency oscillation (LFO) appears in power systems with fast excitation of a unit or large-scale connections. An LFO signal exhibits typical nonstationary and time-varying characteristics. The paper proposed an LFO analyzing technique through the Hilbert spectrum of stationary wavelet transform (SWT). The SWT-based Hilbert spectrum contains time-varying and frequency information of LFO. The analytic framework is introduced, SWT is formulated to decompose an LFO signal into the sum of several coefficient components, the instantaneous parameters of each coefficient are then individually calculated, the complete time-frequency distribution is combined, and finally, the time-varying oscillations characteristics parameters are extracted. The simulation shows that the proposed method effectively reveals time-frequency features of the LFO signal.
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Feng, Chen, and Tang. "Identification of Low Frequency Oscillations Based on Multidimensional Features and ReliefF-mRMR." Energies 12, no. 14 (July 18, 2019): 2762. http://dx.doi.org/10.3390/en12142762.
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Low frequency oscillations (LFOs) in power systems usually fall into two types, i.e., forced oscillations and natural oscillations. Waveforms of the two are similar, but the suppression methods are different. Therefore, it is important to accurately identify LFO type. In this paper, a method for discriminating LFO type based on multi-dimensional features and a feature selection algorithm combining ReliefF and minimum redundancy maximum relevance algorithm (mRMR) is proposed. Firstly, 53 features are constructed from six aspects—time domain, frequency domain, energy, correlation, complexity, and modal analysis—which comprehensively characterize the multidimensional features of LFO. Then, the optimal feature subset with greater relevance and less redundancy is extracted by ReliefF-mRMR. In order to improve the classification performance, a modified Support Vector Machine (SVM) with Genetic Algorithm (GA) optimizing the key parameters is adopted, which is conducted in MATLAB. Finally, in 179-bus system, the samples of LFOs are generated by the Power System Analysis Toolbox (PSAT) and the accuracy of the LFO type identification model is verified. In ISO New England and East China power grid, it is proven that the proposed method can accurately identify LFO type considering the influences of noise, oscillation mode, and data incompletion. Hence, it has good robustness, noise immunity, and practicability.
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Zahalan, Arizadayana, Noor Fazliana Fadzail, and Muhammad Irwanto Misrun. "Comparing the Performance of UPFC Damping Controller on Damping Low Frequency Oscillations." Applied Mechanics and Materials 793 (September 2015): 242–46. http://dx.doi.org/10.4028/www.scientific.net/amm.793.242.
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This paper compares the performance of UPFC damping controller (, and ) to damp Low Frequency Oscillations (LFO) in power system equipped with UPFC based on Fuzzy Logic Power System Stabilizer (UPFC based FLPSS). The power system model was developed using linearized model of Phillips-Heffron Single Machine Infinite Bus (SMIB) and simulated in Matlab Simulink. The ability of each controller to damp LFO present in the rotor speed was monitored when the system being perturbed by small disturbances. The results obtained shown that UPFC controller had better performance to damp LFO compared to the other UPFC damping controllers as it had the lowest overshoot and less settling time.
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Song, Hua, and Yongjun Chen. "Study of WAMS Big Data Elastic Store Model in Low-Frequency Oscillation Analysis." Mathematical Problems in Engineering 2020 (September 22, 2020): 1–8. http://dx.doi.org/10.1155/2020/3541973.
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Low-frequency oscillation (LFO) is among the key factors that threaten interconnected power grids’ security and stability and restrict transfer capability. In particular, power systems incur now and then weak damping and forced oscillations. To monitor and control LFO, the principles of online calculation and analysis of two types of LFO are studied in this paper. The big data of wide area measurements is an important information source of LFO analysis. Hence, we should make sure it has access to online system continuously, accurately, and reliably. Nevertheless, the conventional linear data store model has difficulty to meet the processing requirements of high rate, multiple concurrency, and high reliability. To deal with it, a new model of double-set elastic store is proposed in this paper. It transforms the storage space linear model to plane model, realizes the management of power system substation group sets in vertical direction and the management of multiple Phase Measurement Units (PMU) uploading data sets in horizontal direction, and hence solves the problems in continuous and reliable access of the wide area measurements data, which is dense and of large scale and has quick update rate, providing technical support of accuracy and robustness of LFO analysis. The performance test and practical application of the proposed new model of double-set elastic store validate its accuracy.
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Lu, Chia Liang, and Pei Hwa Huang. "Power System Stability Study with Empirical Mode Decomposition." Advanced Materials Research 732-733 (August 2013): 905–8. http://dx.doi.org/10.4028/www.scientific.net/amr.732-733.905.
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Low frequency oscillations (LFO) reflect the damping and the stability of a power system and is essentially non-stationary. The LFO is a composite response of various oscillation modes and of which the frequency may be changing with time; thus, direct analysis of such time-domain responses is difficult. The main purpose of this paper is to apply the method of empirical mode decomposition (EMD) to the study of power system stability. First the method of EMD is to expand the time-domain responses under study into multiple intrinsic mode functions (IMFs). Then the 2D time-frequency information inherent in the response under study is obtained using the wavelet transform. The 2D time-frequency graph is further expanded into a 3D time-frequency-energy graph. Information from the 3D time-frequency graph is analyzed to determine those generators that have higher extent of oscillation involvement during the occurrence of LFO in the power system. The results from comparative analysis show that, at specific frequencies from LFOs, higher extent of oscillation involvement will reveal a greater factor of involvement in the frequency domain behavior.
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Bicciato, Giulio, Emanuela Keller, Martin Wolf, Giovanna Brandi, Sven Schulthess, Susanne Gabriele Friedl, Jan Folkard Willms, and Gagan Narula. "Increase in Low-Frequency Oscillations in fNIRS as Cerebral Response to Auditory Stimulation with Familiar Music." Brain Sciences 12, no. 1 (December 29, 2021): 42. http://dx.doi.org/10.3390/brainsci12010042.
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Recognition of typical patterns of brain response to external stimuli using near-infrared spectroscopy (fNIRS) may become a gateway to detecting covert consciousness in clinically unresponsive patients. This is the first fNIRS study on the cortical hemodynamic response to favorite music using a frequency domain approach. The aim of this study was to identify a possible marker of cognitive response in healthy subjects by investigating variations in the oscillatory signal of fNIRS in the spectral regions of low-frequency (LFO) and very-low-frequency oscillations (VLFO). The experiment consisted of two periods of exposure to preferred music, preceded and followed by a resting phase. Spectral power in the LFO region increased in all the subjects after the first exposure to music and decreased again in the subsequent resting phase. After the second music exposure, the increase in LFO spectral power was less distinct. Changes in LFO spectral power were more after first music exposure and the repetition-related habituation effect strongly suggest a cerebral origin of the fNIRS signal. Recognition of typical patterns of brain response to specific environmental stimulation is a required step for the concrete validation of a fNIRS-based diagnostic tool.
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Gonzalez-Jimenez, David, Jon Del-Olmo, Javier Poza, Fernando Garramiola, and Patxi Madina. "Data-Driven Low-Frequency Oscillation Event Detection Strategy for Railway Electrification Networks." Sensors 23, no. 1 (December 26, 2022): 254. http://dx.doi.org/10.3390/s23010254.
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Low-frequency oscillations (LFO) occur in railway electrification systems due to the incorporation of new trains with switching converters. As a result, the increased harmonic content can cause catenary stability problems under certain conditions. Most of the research published on this topic to date is focused on modelling the event and analysing it using frequency spectrums. However, in recent years, due to the new technologies linked to Big Data (BD) and data mining (DM), a new opportunity to study and detect LFO events by means of machine-learning (ML) methods has emerged. Trains continuously collect data from the most important catenary variables, which offers new resources for analysing this type of event. Therefore, this article presents the design and implementation of a data-driven LFO event detection strategy for AC railway network scenarios. Compared to previous investigations, a new approach to analyse and detect LFO events, based on field data and ML, is presented. To obtain the most appropriate detection approach for the context of this application, on the one hand, this investigation includes a comparison of machine-learning algorithms (support vector machine, logistic regression, random forest, k-nearest neighbours, naïve Bayes) which have been trained with real field data. On the other hand, an analysis of key parameters and features to optimize event detection is also included. Thus, the most significant result of this work is the high metric values of the solution, reaching values above 97% in accuracy and 93% in F-1 score with the random forest algorithm. In addition, the applicability and training of data-driven methods with real field data are demonstrated. This automatic detection strategy can help with speeding up and improving LFO detection tasks that used to be performed manually. Finally, it is worth mentioning that this research has been structured based on the CRISP-DM methodology, established as the de facto approach for industrial DM projects.
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Neda, Omar Muhammed. "Optimal coordinated design of PSS and UPFC-POD using DEO algorithm to enhance damping performance." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 6111. http://dx.doi.org/10.11591/ijece.v10i6.pp6111-6121.
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Low-frequency oscillations (LFO) are an inevitable problem of power systems and they have a great effect on the capability of transfer and power system stability. The power system stabilizers (PSSs) as well as flexible AC transmission system (FACTS) devices can help to damp LFO. The target of this study is to tackle the problem of a dual-coordinated design between PSS and unified power flow controller (UPFC) implementing the task of power oscillation damping (POD) controller in a single machine infinite bus (SMIB) system. So, dolphin echolocation optimization (DEO) technique is utilized as an optimization tool to search for optimal parameter tunings based on objective function for enhancing the dynamic stability performance for a SMIB. DEO an algorithm has a few parameters, simple rules, provides the optimum result and is applicable to a wide range of problems like other meta-heuristic algorithms. Use DEO gave the best results in damping LFO compared to particle swarm optimization (PSO) algorithm. From the comparison results between PSO and DEO, it was shown that DEO provides faster settling time, less overshoot, higher damping oscillations and greatly improves system stability. Also, the comparison results prove that the multiple stabilizers show supremacy over independent controllers in mitigationg LFO of a SMIB.
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Anarkooli, M. Yousefi, and H. Afrakhteh. "Improvement Model Damping Low Frequency Oscillations Presence UPFC by Cuckoo Optimization Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 1 (July 1, 2016): 67. http://dx.doi.org/10.11591/ijeecs.v3.i1.pp67-79.
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<p>Low frequency oscillation (LFO) is a negative phenomenon repeated for the power system increases the risk of instability. In recent years, power systems stabilizer (PSS) for damping low frequency oscillations is used. With FACTS devices such as integrated power flow controller (UPFC) can control power flow and transient stability increase. So, UPFC low frequency oscillation damping can be used instead of PSS. UPFC through direct control voltage and low frequency oscillation damping can be improved. In this study, a single linear model of synchronous machine connected to an infinite bus Heffron-Philips in the presence of UPFC to improve low frequency oscillation damping is used. The selection of the output feedback parameters for the UPFC controllers is converted to an optimization problem which is solved by cuckoo optimization algorithm (COA). COA, as a new evolutionary optimization algorithm, is used in multiple applications. This optimization algorithm has a strong ability to find the most optimistic results for dynamic stability improvement. The controller UPFC and damping in MATLAB software environment is designed and simulated. The simulation was performed for a variety of loads and for various loads and more effective UPFC controller electromechanical oscillation damping compared to other algorithm types is shown.</p>
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Yin, Liyong, Fan Tian, Rui Hu, Zhaohui Li, and Fuzai Yin. "Estimating Phase Amplitude Coupling between Neural Oscillations Based on Permutation and Entropy." Entropy 23, no. 8 (August 18, 2021): 1070. http://dx.doi.org/10.3390/e23081070.
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Cross-frequency phase–amplitude coupling (PAC) plays an important role in neuronal oscillations network, reflecting the interaction between the phase of low-frequency oscillation (LFO) and amplitude of the high-frequency oscillations (HFO). Thus, we applied four methods based on permutation analysis to measure PAC, including multiscale permutation mutual information (MPMI), permutation conditional mutual information (PCMI), symbolic joint entropy (SJE), and weighted-permutation mutual information (WPMI). To verify the ability of these four algorithms, a performance test including the effects of coupling strength, signal-to-noise ratios (SNRs), and data length was evaluated by using simulation data. It was shown that the performance of SJE was similar to that of other approaches when measuring PAC strength, but the computational efficiency of SJE was the highest among all these four methods. Moreover, SJE can also accurately identify the PAC frequency range under the interference of spike noise. All in all, the results demonstrate that SJE is better for evaluating PAC between neural oscillations.
Conference papers on the topic "Low-frequency oscillations (LFO)"
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Aleikish, Khaled, and Thomas Øyvang. "LSTM-based PSS Design for Modern Power Systems." In 63rd International Conference of Scandinavian Simulation Society, SIMS 2022, Trondheim, Norway, September 20-21, 2022. Linköping University Electronic Press, 2022. http://dx.doi.org/10.3384/ecp192023.
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With the ever-increasing incorporation of wind and solar power in the electric power system, enhanced performance of classical bulk hydropower plants for robust operation of the power system is required. This current energy transition may cause a rapid increase in undesirable low-frequency oscillations (LFOs) in modern power system operations. A power system stabilizer (PSS) located at hydropower plants is one solution to damp such oscillations. This paper presents a new method based on Long Short-Term Memory (LSTM) neural networks for sine-wave phase shifting to possibly enhance PSS damping. The proposed controller considers the PSS input and the rotor speed deviation as a damped sinusoidal signal, simplifying PSS control and real-time optimization of PSSs parameters. Results show that the proposed LSTM architecture is able to learn multiple damped sine waves with different frequencies and decay rates. Therefore, the proposed controller can operate on the entire range of LFOs, unlike simple feedforward neural network (FNN) controllers, which can only learn and function on a single LFO frequency.
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Hosseini, Hossein, Behrooz Tusi, Navid Razmjooy, and Mohsen Khalilpoor. "Optimum design of PSS and SVC controller for damping low frequency oscillation (LFO)." In 2011 2nd International Conference on Control, Instrumentation, and Automation (ICCIA). IEEE, 2011. http://dx.doi.org/10.1109/icciautom.2011.6356631.
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Yi, Tongxun, and Ephraim J. Gutmark. "Real-Time Prediction of Incipient Lean Blowout in a Partially Premixed, Liquid-Fueled Gas Turbine Combustor." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27674.
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The present paper addresses real-time prediction of incipient lean blowout (LBO) in partially premixed, liquid-fueled gas turbine combustors. Near-LBO combustion is characterized by the “intensified” low-frequency combustion oscillations, typically below 30 Hz. Two indices, namely the normalized chemiluminescence RMS and the normalized cumulative duration of LBO precursor events, are recommended for LBO prediction. Both indices are associated with the statistical characteristics of the flame structure, which changes from the normal distribution to the Rayleigh distribution at the approach of LBO. Both indices change little within a large range of equivalence ratios and start to shoot up only when LBO is approached. To use the two indices for LBO prediction, one needs to perform a detailed a priori LBO mapping under simulated engine operating conditions. However, the mapping can be done without running the engines very close to LBO.
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Berthold, Christian, Johann Gross, Christian Frey, and Malte Krack. "Analysis of Friction-Saturated Flutter Vibrations With a Fully-Coupled Frequency Domain Method." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-16253.
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Abstract Flutter stability is a dominant design constraint of modern gas and steam turbines. Thus, flutter-tolerant designs are currently explored, where the resulting vibrations remain within acceptable bounds. In particular, friction damping has the potential to yield Limit Cycle Oscillations (LCOs) in the presence of a flutter instability. To predict such LCOs, it is the current practice to model the aerodynamic forces in terms of aerodynamic influence coefficients, derived for some normal modes of the linearized structural model and fixed oscillation frequency. However, this approach neglects that both the nonlinear contact interactions and the aerodynamic stiffness cause a change in the deflection shape and the frequency of the LCO. This, in turn, may have a substantial effect on the aerodynamic damping. The goal of this paper is to assess the technical importance of these neglected interactions. To this end, a state-of-the-art aero-elastic model of a low pressure turbine blade row is considered, undergoing nonlinear frictional contact interactions in the tip shroud interfaces. The LCOs are computed with a fully-coupled harmonic balance method, which iteratively computes the Fourier coefficients of structural deformation and conservative flow variables, as well as the a priori unknown frequency. The coupled algorithm was tested for various combinations of harmonics in both domains and found to provide excellent computational robustness and efficiency. Moreover, a refinement of the conventional energy method is developed and assessed, which accounts for both the nonlinear contact boundary conditions and the linearized aerodynamic influence. It is found that the conventional energy method may not predict a limit cycle oscillation at all while the novel approach presented here can. Furthermore the refined energy method provides deep understanding of the nonlinear aero-elastic interactions.
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Gadiraju, Siddhartha, Suhyeon Park, David Gomez-Ramirez, Srinath V. Ekkad, K. Todd Lowe, Hee-Koo Moon, Yong Kim, and Ram Srinivasan. "Application of Proper Orthogonal Decomposition to High Speed Imaging for the Study of Combustion Oscillations." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-64602.
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The flame structure and characteristics generated by an industrial low emission, lean premixed, fuel swirl nozzle were analyzed for understanding combustion oscillations. The experimental facility is located at the Advanced Propulsion and Power Laboratory (APPL) at Virginia Tech. The experiments were carried out in a model optical can combustor operating at atmospheric pressures. Low-frequency oscillations (<100 Hz) were observed during the reaction as opposed to no reaction, cold flow test cases. The objective of this paper is to understand the frequency and magnitude of oscillations due to combustion using high-speed imaging and associate them with corresponding structure or feature of the flame. Flame images were obtained using a Photron Fastcam SA4 high-speed camera at 500 frames per second. The experiments were conducted at equivalence ratios of 0.65, 0.75; different Reynolds numbers of 50K, 75K; and three pilot fuel to main fuel ratios of 0%, 3%, 6%. In this study, Reynolds number was based on the throat diameter of the fuel nozzle. Since the time averaged flame images are not adequate representation of the flame structures, proper orthogonal decomposition (POD) was applied to the flame images to extract the dominant features. The spatiotemporal dynamics of the images can be decomposed into their constituent modes of maximum spatial variance using POD so that the dominant features of the flame can be observed. The frequency of the dominant flame structures, as captured by the POD modes of the flame acquisitions, were consistent with pressure measurements taken at the exit of the combustor. Thus, the oscillations due to combustion can be visualized using POD. POD was further applied to high-speed images taken during instabilities. Specifically, the instabilities discussed in this paper are those encountered when the equivalence ratio is reduced to the levels approaching lean blowout (LBO). As the equivalence ratio is reduced to near blowout regime, it triggers low-frequency high amplitude instabilities. These low-frequency instabilities are visible as the flapping of the flame. The frequencies of the dominant POD modes are consistent with pressure measurements recorded during these studies.
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Gan, Jiaye, Hong-Sik Im, and Ge-Cheng Zha. "Numerical Examination of Lock-In Hypothesis of Non-Synchronous Vibration in an Axial Compressor." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-65244.
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This paper examines the lock-in hypothesis of non-synchronous vibration (NSV) in a high speed multistage axial compressor. The unsteady Reynolds-averaged Navier-Stokes (URANS) equations and modal approach based structural dynamic equations are solved. A low diffusion E-CUSP approximate Riemann solver with a 3rd order WENO scheme for the inviscid fluxes and a 2nd order central differencing for the viscous terms are employed. The structural vibration of the blades are solved by a set of modal equations that are fully coupled with the flow equation. The rigid blade simulations are conducted to examine the main driver of NSV. A 1/7th annulus sector of IGV-rotor-stator is used with a time shifted phase lag BC at circumferential boundaries. A dominant excitation frequency caused by the traveling tip vortices are captured. The excitation frequency is not on the engine order. The simulation is then switched to fluid structure interaction that allows the blades to vibrate freely under the flow excitations. The matching of aerodynamic forcing frequency with the structure response frequency seems indicating that the NSV of this compressor is a limit cycle oscillation (LCO) excited by aerodynamic forcing, not caused by flow frequency/phase locked to structural frequency. The rotating speed is varied within a RPM range, in which the rig test detected the NSV. The unsteady flows with rigid blades are simulated first at several RPMs. The simulation indicates that the structure response follows the frequency of the flow excitations existing in the rigid blades. At least under the simulated conditions, the NSV does not appear to be a lock-in phenomenon, which has the flow frequency lock-in to the structure natural frequency.
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Berthold, Christian, Johann Groß, Christian Frey, and Malte Krack. "Fully Coupled Analysis of Flutter Induced Limit Cycles: Frequency vs. Time Domain Methods." In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-77999.
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Abstract To save resources and reduce emissions, it is crucial to reduce weight of aircraft engines and further increase aerodynamic efficiency of gas and steam turbines. For turbine blades these goals often lead to flutter. Thus, innovative flutter-tolerant designs are explored, where flutter induces limit cycle oscillations (LCOs) of tolerable yet nonzero level. Flutter represents a self-excitation mechanism and, in the linear case, would lead to exponential divergence. Flutter-induced LCOs are therefore an inherently nonlinear phenomenon. The saturation of flutter-induced vibrations can be caused by nonlinear frictional contact interactions e.g. in tip shroud interfaces. To develop flutter-tolerant designs, efficient methods are required which compute LCOs based on an appropriate modeling of elastic, inertia, aerodynamic and contact forces. We recently developed a Frequency Domain Fluid-Structure Interaction (FD-FSI) solver for flutter-induced LCOs. The solver relies on the Harmonic Balance method applied to the structure as well as the fluid domain. It was shown that especially for long and slender blades with friction in shroud interfaces and strong aerodynamic influence, a coupled analysis can significantly increase the accuracy of predicted LCOs compared to the current state-of-the-art methods. Conventional methods do not properly account for the nonlinear change of frequency and deflection shape, and the effect of these changes on the aerodynamic damping, and thus fail in predicting certain LCOs at all. In the current work the FD-FSI solver is numerically validated against Time Domain Fluid-Structure Interaction (TD-FSI) simulations. As a test case a shrouded low pressure turbine with friction in the shroud interfaces is considered. The point of operation is highly loaded and transonic in order to make the test case challenging. Apart from a successful validation of the FD-FSI solver, we shed light on important advantages and disadvantages of both solvers. Due to the lack of robust phase-lag boundary conditions for time domain solvers, a full blade row must be simulated. Thus, the FD-FSI solver typically requires only a fraction of the computational costs. Moreover, the FD-FSI solver contributes to an increased physical understanding of the coupled vibrations: By analyzing the contribution of individual harmonics, we analyze why unexpected even harmonics appear in a certain LCO. On the other hand, the FD-FSI solver does not provide information on the asymptotic stability of the LCOs and is strictly limited to periodic oscillations. Indeed, quasi-periodic limit torus oscillations (LTOs) appear in our test case. Using the TD-FSI solver, we confirm the internal combination resonance, postulated recently as necessary condition for LTOs, for the first time, in a fully coupled analysis.