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Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Flutter Prediction"

1

Dimitriadis, G., and J. E. Cooper. "Flutter Prediction from Flight Flutter Test Data." Journal of Aircraft 38, no. 2 (March 2001): 355–67. http://dx.doi.org/10.2514/2.2770.

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2

Sudha, U. P. V., G. S. Deodhare, and K. Venkatraman. "A comparative assessment of flutter prediction techniques." Aeronautical Journal 124, no. 1282 (October 27, 2020): 1945–78. http://dx.doi.org/10.1017/aer.2020.84.

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ABSTRACTTo establish flutter onset boundaries on the flight envelope, it is required to determine the flutter onset dynamic pressure. Proper selection of a flight flutter prediction technique is vital to flutter onset speed prediction. Several methods are available in literature, starting with those based on velocity damping, envelope functions, flutter margin, discrete-time Autoregressive Moving Average (ARMA) modelling, flutterometer and the Houbolt–Rainey algorithm. Each approach has its capabilities and limitations. To choose a robust and efficient flutter prediction technique from among the velocity damping, envelope function, Houbolt–Rainey, flutter margin and auto-regressive techniques, an example problem is chosen for their evaluation. Hence, in this paper, a three-degree-of-freedom model representing the aerodynamics, stiffness and inertia of a typical wing section is used(1). The aerodynamic, stiffness and inertia properties in the example problem are kept the same when each of the above techniques is used to predict the flutter speed of this aeroelastic system. This three-degree-of-freedom model is used to generate data at speeds before initiation of flutter, during flutter and after occurrence of flutter. Using these data, the above-mentioned flutter prediction methods are evaluated and the results are presented.
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3

Gabriela, STROE, and ANDREI Irina-Carmen. "STUDIES ON FLUTTER PREDICTION." INCAS BULLETIN 4, no. 1 (March 9, 2012): 115–23. http://dx.doi.org/10.13111/2066-8201.2012.4.1.12.

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4

CANFIELD, ROBERT A., RAYMOND G. TOTH, and REID MELVILLE. "VIBRATION AND TRANSONIC FLUTTER ANALYSIS FOR F-16 STORES CONFIGURATION CLEARANCE." International Journal of Structural Stability and Dynamics 06, no. 03 (September 2006): 377–95. http://dx.doi.org/10.1142/s0219455406002039.

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This paper supports quick and accurate prediction of the flutter onset speed of an F-16 Block 40/50 configured with external stores in the transonic flight regime. Current flutter prediction methods are reviewed and hypothesized mechanisms for limit cycle oscillation (LCO) are summarized. New efforts to correlate transonic small disturbance (TSD) theory methods with flight tests are outlined. Vibration analysis and structural optimization of an F-16 finite element model were used to match ground vibration testing results. Frequency tuning was found to be critical for accurate flutter speed predictions. Sensitivity to nonlinear aerodynamic effects and store modeling was examined.
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5

Chi, R. M., and A. V. Srinivasan. "Some Recent Advances in the Understanding and Prediction of Turbomachine Subsonic Stall Flutter." Journal of Engineering for Gas Turbines and Power 107, no. 2 (April 1, 1985): 408–17. http://dx.doi.org/10.1115/1.3239741.

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In this paper, some recent advances in the understanding and prediction of subsonic flutter of jet engine fan rotor blades are reviewed. Among the topics discussed are (i) the experimental evidence of mistuning in flutter responses, (ii) new and promising unsteady aerodynamic models for subsonic stall flutter prediction, (iii) an overview of flutter prediction methodologies, and (iv) a new research effort directed toward understanding the mistuning effect on subsonic stall flutter of shrouded fans. A particular shrouded fan of advanced design is examined in the detailed technical discussion.
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6

Sun, Zhi Wei, and Jun Qiang Bai. "Time-Domain Aeroservoelastic Modeling and Active Flutter Suppression by Model Predictive Control." Advanced Materials Research 898 (February 2014): 688–95. http://dx.doi.org/10.4028/www.scientific.net/amr.898.688.

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A time-domain aeroservoelastic model is developed to calculate the flutter speed and an active flutter suppression system is designed by model predictive control. The finite-state, induced-flow theory and equilibrium beam finite element method are chosen to formulate the aeroservoelastic governing equations in state-space form, which is necessary for active flutter suppression design with modern control theory. A sensitivity analysis is performed to find the most appropriate number of induced-flow terms and beam elements. Model predictive control theory is adopted to design an active flutter suppression system due to its ability to deal with the constraints on rate change and amplitude of input. The numerical result shows a satisfactory precision of the flutter speed prediction, the close loop analysis shows that the flutter boundary is considerable expanded.
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7

Dimitriadis, G., and J. E. Cooper. "Comment on "Flutter Prediction from Flight Flutter Test Data"." Journal of Aircraft 43, no. 3 (May 2006): 862–63. http://dx.doi.org/10.2514/1.c9463tc.

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8

Bae, Jae-Sung, Jong-Yun Kim, In Lee, Yuji Matsuzaki, and Daniel J. Inman. "Extension of Flutter Prediction Parameter for Multimode Flutter Systems." Journal of Aircraft 42, no. 1 (January 2005): 285–88. http://dx.doi.org/10.2514/1.6440.

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9

Arifianto, Dhany. "Flutter prediction on combined EPS and carbon sandwich structure for light aircraft wing." Journal of the Acoustical Society of America 150, no. 4 (October 2021): A345. http://dx.doi.org/10.1121/10.0008533.

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Flutter prediction is an important step before conducting a flight test. In this study, we performed flutter prediction of a half-wing structure without control surfaces. The half-wing structure is made to resemble the scaled-down wing of a Boeing 737 NG at a scale of 1:39.34. The airfoil profile used is the wing profile of the Boeing 737 NG obtained from airfoiltools. The structure is constructed using a combination of carbon sandwich andEPS. The advantages of choosing this material are its low-cost and easymanufacture. We used the p-k method in the FEMAP software for flutter prediction. From the prediction results, the calculated flutter speed is ∼14.5 m/s. The flutter mode shape is a combination of lateral bending and twist. Dimensional analysis was also carried out to predict the maximum speed on the scaled model and predicted at 27.88 m/s. Based on calculated flutter speed, the maximum operating speed of a constructed structure should be far less than the flutter speed. Thus, the structure's maximum speed is below the predicted value. Based on carried out flutter prediction, the wing structure, constructed using a combination of carbon sandwich and EPS, can fly safely at a maximum cruise speed of 10 m/s.
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10

Zheng, Hua, Junhao Liu, and Shiqiang Duan. "Novel Nonstationarity Assessment Method for Hypersonic Flutter Flight Tests." Mathematical Problems in Engineering 2018 (October 25, 2018): 1–12. http://dx.doi.org/10.1155/2018/9742591.

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Hypersonic aircraft have been rapidly developed in recent years both theoretically and experimentally. Aerothermoelastic simulation is very challenging due to its inherent complexity, but physical tests are a workable approach. Flutter tests with variable speed are a popular alternative to hypersonic tests which provide nonstationary structural response data. This paper proposes a nonstationarity assessment method based on energy distribution in the time-frequency domain. The proposed method reveals the nonstationarity level corresponding to the appropriate modal identification algorithm or flutter boundary prediction (FBP) method. Several classic flutter criteria are utilized to build a hypersonic aircraft FBP framework. Numerical simulation and experimental applications demonstrate the effectiveness and feasibility of the proposed method, which facilitates accurate flutter predictions for the subcritical turbulence response during hypersonic flutter flight.
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Дисертації з теми "Flutter Prediction"

1

Perrocheau, Mathilde. "Flutter Prediction in Transonic Regime." Thesis, KTH, Flygdynamik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234840.

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The flutter is a dangerous aeroelastic instability that can cause dramatic failures. It is important to evaluate in which conditions it can occur to ensure the safety of the pilots and the passengers. As flight tests are very expensive and hazardous, the need for efficient and trustworthy numerical tools becomes essential. This report focuses on two methods to predict the flutter conditions in the transonic domain. To evaluate the accuracy of these tools, their results are compared to experimental data gathered during a wind-tunnel test. The influence of the Mach number and the angle of attack on the flutter conditions is studied and physical explanations are put forward.
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2

Turevskiy, Arkadiy 1974. "Flutter boundary prediction using experimental data." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50327.

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3

Yildiz, Erdinc Nuri. "Aeroelastic Stability Prediction Using Flutter Flight Test Data." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608623/index.pdf.

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Анотація:
Flutter analyses and tests are the major items in flight certification efforts required when a new air vehicle is developed or when a new external store is developed for an existing aircraft. The flight envelope of a new aircraft as well as the influence of aircraft modifications on an existing flight envelope can be safely determined only by flutter tests. In such tests, the aircraft is instrumented by accelerometers and exciters. Vibrations of the aircraft at specific dynamic pressures are measured and transmitted to a ground station via telemetry systems during flutter tests. These vibration data are analyzed online by using a flutter test software with various methods implemented in order to predict the safety margin with respect to flutter. Tests are performed at incrementally increasing dynamic pressures and safety regions of the flight envelope are determined step by step. Since flutter is a very destructive instability, tests are performed without getting too close to the flutter speed and estimations are performed by extrapolation. In this study, pretest analyses and flutter prediction methods that can be used in various flight conditions are investigated. Existing methods are improved and their applications are demonstrated with experiments. A novel method to predict limit cycle oscillations that are encountered in some modern fighter aircraft is developed. The prediction method developed in this study can effectively be used in cases where the nonlinearities in aircraft dynamics and air flow reduce the applicability of the classical prediction methods. Some further methods to reduce the adverse effects of these nonlinearities on the predictions are also developed.
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4

Shieh, Teng-Hua. "Prediction and analysis of wing flutter at transonic speeds." Diss., The University of Arizona, 1991. http://hdl.handle.net/10150/185694.

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This dissertation deals with the instability, known as flutter, of the lifting and control surfaces of aircraft of advanced design at high altitudes and speeds. A simple model is used to represent the aerodynamics for flutter analysis of a two-degree-of-freedom airfoil system. Flutter solutions of this airfoil system are shown to be algebraically homomorphic in that solutions about different elastic axes can be found by mapping them to those about the mid-chord. Algebraic expressions for the flutter speed and frequency are thus obtained. For the prediction of flutter of a wing at transonic speeds, an accurate and efficient computer code is developed. The unique features of this code are the capability of accepting a steady mean flow regardless of its origin, a time dependent perturbation boundary condition for describing wing deformations on the mean surface, and a locally applied three-dimensional far-field boundary condition for minimizing wave reflections from numerical boundaries. Results for various test cases obtained using this code show good agreement with the experiments and other theories.
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5

Sun, Tianrui. "Improved Flutter Prediction for Turbomachinery Blades with Tip Clearance Flows." Licentiate thesis, KTH, Energiteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233770.

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Recent design trends in steam turbines strive for high aerodynamic loading and high aspect ratio to meet the demand of higher efficiency. These design trends together with the low structural frequency in last stage steam turbines increase the susceptibility of the turbine blades to flutter. Flutter is the self-excited and self-sustained aeroelastic instability phenomenon, which can result in rapid growth of blade vibration amplitude and eventually blade failure in a short period of time unless adequately damped. To prevent the occurrences of flutter before the operation of new steam turbines, a compromise between aeroelastic stability and stage efficiency has to be made in the steam turbine design process. Due to the high uncertainty in present flutter prediction methods, engineers use large safety margins in predicting flutter which can rule out designs with higher efficiency. The ability to predict flutter more accurately will allow engineers to push the design envelope with greater confidence and possibly create more efficient steam turbines. The present work aims to investigate the influence of tip clearance flow on the prediction of steam turbine flutter characteristics. Tip clearance flow effect is one of the critical factors in flutter analysis for the majority of aerodynamic work is done near the blade tip. Analysis of the impact of tip clearance flow on steam turbine flutter characteristics is therefore needed to formulate a more accurate aeroelastic stability prediction method in the design phase.Besides the tip leakage vortex, the induced vortices in the tip clearance flow can also influence blade flutter characteristics. However, the spatial distribution of the induced vortices cannot be resolved by URANS method for the limitation of turbulence models. The Detached-Eddy Simulation (DES) calculation is thus applied on a realistic-scale last stage steam turbine model to analyze the structure of induced vortices in the tip region. The influence of the tip leakage vortex and the induced vortices on flutter prediction are analyzed separately. The KTH Steam Turbine Flutter Test Case is used in the flutter analysis as a typical realistic-scale last stage steam turbine model. The energy method based on 3D unsteady CFD calculation is applied in the flutter analysis. Two CFD solvers, an in-house code LUFT and a commercial software ANSYS CFX, are used in the flutter analysis as verification of each other. The influence of tip leakage vortex on the steam turbine flutter prediction is analyzed by comparing the aeroelastic stability of two models: one with the tip gap and the other without the tip gap. Comparison between the flutter characteristics predicted by URANS and DES approaches is analyzed to investigate the influence of the induced vortices on blade flutter characteristics. The multiple induced vortices and their relative rotation around the tip leakage vortex in the KTH Steam Turbine Flutter Test Case are resolved by DES but not by URANS simulations. Both tip leakage vortex and induced vortices have an influence on blade loading on the rear half of the suction side near the blade tip. The flutter analysis results suggest that the tip clearance flow has a significant influence on blade aerodynamic damping at the least stable interblade phase angle (IBPA), while its influence on the overall shape of the damping curve is minor. At the least stable IBPA, the tip leakage vortex shows a stabilization effect on rotor aeroelastic stabilities while the induced vortices show a destabilization effect on it. Meanwhile, a non-linear unsteady flow behavior is observed due to the streamwise motion of induced vortices during blade oscillation, which phenomenon is only resolved in DES results.
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6

Opgenoord, Max Maria Jacques. "Transonic flutter prediction and aeroelastic tailoring for next-generation transport aircraft." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120380.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 121-141) and index.
Novel commercial transport aircraft concepts feature large wing spans to increase their fuel efficiency; these wings are more flexible, leading to more potential aeroelastic problems. Furthermore, these aircraft fly in the transonic flow regime, where utter prediction is difficult. The goals for this thesis are to devise a method to reduce the computational burden of including transonic utter constraints in conceptual design tools, and to offer a potential solution for mitigating utter problems through the use of additive manufacturing techniques, specically focusing on a design methodology for lattice structures. To reduce the computational expense of considering transonic utter in conceptual aircraft design, a physics-based low-order method for transonic utter prediction is developed, which is based on small unsteady disturbances about a known steady flow solution. The states of the model are the circulation and doublet perturbations, and their evolution equation coefficients are calibrated using off-line unsteady two-dimensional flow simulations. The model is formulated for swept high-aspect ratio wings through strip theory and 3D corrections. The resulting low-order unsteady flow model is coupled to a typical-section structural model (for airfoils) or a beam model (for wings) to accurately predict utter of airfoils and wings. The method is fast enough to permit incorporation of transonic utter constraints in conceptual aircraft design calculations, as it only involves solving for the eigenvalues of small state-space systems. This model is used to describe the influence of transonic utter on next generation aircraft configurations, where it was found that transonic utter constraints can limit the eciency gains seen by better material technology. As a potential approach for mitigating utter, additively manufactured lattice structures are aeroelastically tailored to increase the flutter margin of wings. Adaptive meshing techniques are used to design the topology of the lattice to align with the load direction while adhering to manufacturing constraints, and the lattice is optimized to minimize the structural weight and to improve the flutter margin. The internal structure of a wing is aeroelastically tailored using this design strategy to increase the flutter margin, which only adds minimal weight to the structure due to the large design freedom the lattice structure offers.
by Max Maria Jacques Opgenoord.
Ph. D.
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7

Erives, Anchondo Ruben. "Validation of non-linear time marching and time-linearised CFD solvers used for flutter prediction." Thesis, KTH, Kraft- och värmeteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175542.

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The turbomachinery related industry relies heavily on numerical tools for the design and development of modern turbomachines. In order to be competitive turbomachines ought to be highly efficient and robust. This has lead engineers to develop more aggressive designs, which often leads to lower margins of structural reliability.  One of the strongest threats to turbomachines are high cycle fatigue problems which arise from aeroelastic phenomena such as flutter. According to Kielb R. (2013) many of such problems are detected at developing testing stage. This implies that the prediction capabilities for aeroelastic phenomena are in need of further development and/or tuning. This is especially evident for unsteady flow phenomena at transonic regimes. A very important step for the improvement of unsteady aerodynamic solvers is the validation and comparison of such solvers. The present thesis concerns with the validation and comparison of a non-linear time marching (ANSYS CFX) and the GKN’s in-house linearised solvers used for flutter analysis. The former has recently implemented a new feature called Transient Blade Row TBR, which drastically reduces the simulation domain to a maximum of two blades.  In order to be included in the deign process, such tool need to be validated. In the same way, the recently launched in-house code LINNEA needs to be validated in order to be considered as a design tool. Experimental data from the aeroelastic standard configuration 4, and the FUTURE project were used for the validation purposes. The validation process showed that the solvers agreed very well between them for the standard configuration. Such agreement was less clear for the FUTURE compressor; nonetheless, the solutions still sit within the bulk of solutions provided from the different FUTURE partners. The validation showed that these tools provide with similar results as the state of the art tools from different companies. This indicates that they can be used in the design process. At the same time it was observed that there is room for improvement in the solvers, as these still present some considerable differences with the experimental results.
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8

Delamore-Sutcliffe, David William. "Modelling of unsteady stall aerodynamics and prediction of stall flutter boundaries for wings and propellers." Thesis, University of Bristol, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440048.

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9

Kassem, H. I. "Flutter prediction of metallic and composite wings using coupled DSM-CFD models in transonic flow." Thesis, City, University of London, 2017. http://openaccess.city.ac.uk/20404/.

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Although flutter analysis is a relatively old problem in aviation, it is still challenging, particularly with the advent of composite materials and requirements for high-speed light airframes. The main challenge for this problem is at the transonic flow region. The transonic flow, being non-linear, poses a great challenge over traditional linear theories which fail to predict the aerodynamic properties accurately. Aerospace has been one of the primary areas of applications to take advantage of composite materials with the aim to reduce the total mass and improve control effectiveness. This work takes advantage of CFD methods advancement as the main flow solver for non-linear governing equations. In order to investigate the dynamic behaviour of composite aircraft wings, the dynamic stiffness method (DSM) for bending-torsion composite beam is used to compute the free vibration natural modes. The main objective of this work is coupling the dynamic stiffness method (DSM) with high fidelity computational fluid dynamics models in order to predict the transonic flutter of composite aircraft wings accurately and efficiently. In addressing the main aim of this study, Euler fluid flow solvers of an open source CFD code called OpenFOAM has been coupled with elastic composite wing, represented by the free vibration modes computed by DSM. The first part of this study is devoted to investigating the free vibration characteristics of two types of aircraft, namely sailplane type and transport airliner type. Two models of each type have been analysed and contrasted, which revealed the significance of the natural modes of aircraft wings and how these modes inherently capture the essential characteristics of the system. Then to validate the CFD code, two pitching and self-sustained two degrees of freedom airfoils under different flow condition have been modelled. The results have been compared against experimental measurements and numerical data from the literature which showed good agreement for the predicted force coefficients. Finally, the model has been extended to study a complete aircraft wing. Both metallic and composite Goland wings have been investigated under a wide range of flow conditions. The composite wing has been investigated using different material coupling values to show their effect on its aeroelastic behaviour. The results showed the significant influence of the material coupling on the aeroelastic characteristics of composite wings.
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10

Perry, Brendan. "Predictions of flutter at transonic speeds." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.498853.

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Книги з теми "Flutter Prediction"

1

J, Brenner Martin, and United States. National Aeronautics and Space Administration., eds. A worst-case approach for on-line flutter prediction. [Washington, D.C: National Aeronautics and Space Administration, 1998.

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2

J, Brenner Martin, and United States. National Aeronautics and Space Administration., eds. A worst-case approach for on-line flutter prediction. [Washington, D.C: National Aeronautics and Space Administration, 1998.

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3

A, Simons Todd, and NASA Glenn Research Center, eds. Application of TURBO-AE to flutter prediction: Aeroelastic code development. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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4

A, Simons Todd, and NASA Glenn Research Center, eds. Application of TURBO-AE to flutter prediction: Aeroelastic code development. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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5

A, Simons Todd, and NASA Glenn Research Center, eds. Application of TURBO-AE to flutter prediction: Aeroelastic code development. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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6

V, Kaza K. R., and United States. National Aeronautics and Space Administration., eds. Semi-empirical model for prediction of unsteady forces on an airfoil with application to flutter. [Washington, DC]: National Aeronautics and Space Administration, 1992.

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7

V, Kaza K. R., and United States. National Aeronautics and Space Administration., eds. Semi-empirical model for prediction of unsteady forces on an airfoil with application to flutter. [Washington, DC]: National Aeronautics and Space Administration, 1992.

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8

Paduano, James D. Methods for in-flight robustness evaluation: Summary of research. [Washington, DC: National Aeronautics and Space Administration, 1995.

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9

1945-, Bennett Robert M., and Langley Research Center, eds. Using transonic small disturbance theory for predicting the aeroelastic stability of a flexible wind-tunnel model. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1990.

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10

Eric, Feron, Brenner Marty, and United States. National Aeronautics and Space Administration., eds. Methods for in-flight robustness evaluation: Summary of research. [Washington, DC: National Aeronautics and Space Administration, 1995.

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Частини книг з теми "Flutter Prediction"

1

Promio, Charles F., T. S. Varalakshmi, Pooja Bhat, G. A. Vedavathi, and V. Sushma. "Unsteady aerodynamic force approximation for flutter prediction." In Aerospace and Associated Technology, 366–71. London: Routledge, 2022. http://dx.doi.org/10.1201/9781003324539-67.

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2

Kumar, A. Arun, and Amit Kumar Onkar. "Robust Flutter Prediction of an Airfoil Including Uncertainties." In Lecture Notes in Mechanical Engineering, 305–14. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9601-8_22.

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3

Sévérin*, Tinmitonde, He Xuhui, and Yan Lei. "Prediction of flutter velocity of long-span bridges using probabilistic approach." In Current Perspectives and New Directions in Mechanics, Modelling and Design of Structural Systems, 90–95. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003348443-14.

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4

Tinmitonde, S., X. He, and L. Yan. "Prediction of flutter velocity of long-span bridges using probabilistic approach." In Current Perspectives and New Directions in Mechanics, Modelling and Design of Structural Systems, 31–32. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003348450-14.

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Banavara, Nagaraj K., and Diliana Dimitrov. "Prediction of Transonic Flutter Behavior of a Supercritical Airfoil Using Reduced Order Methods." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 365–73. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03158-3_37.

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6

Hebler, Anne, and Reik Thormann. "Flutter Prediction of a Laminar Airfoil Using a Doublet Lattice Method Corrected by Experimental Data." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 445–55. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27279-5_39.

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7

Arena, Andrew S., and Kajal K. Gupta. "Expediting time-marching supersonic flutter prediction through a combination of CFD and aerodynamic modeling techniques." In Fifteenth International Conference on Numerical Methods in Fluid Dynamics, 268–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0107113.

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Zhou, R., Y. J. Ge, Y. Yang, Y. D. Du, and L. H. Zhang. "Nonlinear Wind-Induced Vibration Behaviors of Multi-tower Suspension Bridges Under Strong Wind Conditions." In Lecture Notes in Civil Engineering, 1–10. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3330-3_1.

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Анотація:
AbstractThe aerodynamic characteristics of a multispan suspension bridge differ from those of a two-span suspension bridge. In this study we investigated the nonlinear aerodynamic characteristics of the Maanshan Bridge under nonstationary flow using combination quasi-3D finite element (FE) bridge models of 2D nonlinear aerodynamic force models and 3D nonlinear FE bridge models. The developed model predictions were validated by wind tunnel tests involving a 2D sectional stiffness model and 3D full-bridge aeroelastic model. Results showed that the developed model could potentially describe the nonlinear and unsteady aerodynamic effects on the bridge. Furthermore, the flutter behavior of the Maanshan Bridge under uniform flow changed from the stable limit cycle of soft flutter to unstable limit cycle with the disconnection of two hangers at the 1/2L of the right main span, while the flutter behavior of the bridge under turbulence flow could be defined as the fracture failure of the hangers from the 1/2L of the left main span.
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Georgiou, Georgia, Hamed Haddad Khodaparast, and Jonathan E. Cooper. "Uncertainty Quantification of Aeroelastic Stability." In Advances in Computational Intelligence and Robotics, 329–56. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4991-0.ch016.

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The application of uncertainty analysis for the prediction of aeroelastic stability, using probabilistic and non-probabilistic methodologies, is considered in this chapter. Initially, a background to aeroelasticity and possible instabilities, in particular “flutter,” that can occur in aircraft is given along with the consideration of why Uncertainty Quantification (UQ) is becoming an important issue to the aerospace industry. The Polynomial Chaos Expansion method and the Fuzzy Analysis for UQ are then introduced and a range of different random and quasi-random sampling techniques as well as methods for surrogate modeling are discussed. The implementation of these methods is demonstrated for the prediction of the effects that variations in the structural mass, resembling variations in the fuel load, have on the aeroelastic behavior of the Semi-Span Super-Sonic Transport wind-tunnel model (S4T). A numerical model of the aircraft is investigated using an eigenvalue analysis and a series of linear flutter analyses for a range of subsonic and supersonic speeds. It is shown how the Probability Density Functions (PDF) of the resulting critical flutter speeds can be determined efficiently using both UQ approaches and how the membership functions of the aeroelastic system outputs can be obtained accurately using a Kriging predictor.
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Kanani, Pratik, and Mamta Chandraprakash Padole. "ECG Image Classification Using Deep Learning Approach." In Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning, 343–57. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2742-9.ch016.

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Cardiovascular diseases are a major cause of death worldwide. Cardiologists detect arrhythmias (i.e., abnormal heart beat) with the help of an ECG graph, which serves as an important tool to recognize and detect any erratic heart activity along with important insights like skipping a beat, a flutter in a wave, and a fast beat. The proposed methodology does ECG arrhythmias classification by CNN, trained on grayscale images of R-R interval of ECG signals. Outputs are strictly in the terms of a label that classify the beat as normal or abnormal with which abnormality. For training purpose, around one lakh ECG signals are plotted for different categories, and out of these signal images, noisy signal images are removed, then deep learning model is trained. An image-based classification is done which makes the ECG arrhythmia system independent of recording device types and sampling frequency. A novel idea is proposed that helps cardiologists worldwide, although a lot of improvements can be done which would foster a “wearable ECG Arrhythmia Detection device” and can be used by a common man.
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Тези доповідей конференцій з теми "Flutter Prediction"

1

Ueda, Tetsuhiko, Masanobu IIo, and Tadashige Ikeda. "Flutter Prediction Using Wavelet Transform." In 48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/6.2007-2320.

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2

Lowe, Brandon, and David W. Zingg. "Flutter Prediction using Reduced-Order Modeling." In AIAA Scitech 2020 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2020. http://dx.doi.org/10.2514/6.2020-1998.

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3

Tamayama, Masato, Hitoshi Arizono, Kenichi Saitoh, and Norio Yoshimoto. "Development of flutter margin prediction program." In 9TH INTERNATIONAL CONFERENCE ON MATHEMATICAL PROBLEMS IN ENGINEERING, AEROSPACE AND SCIENCES: ICNPAA 2012. AIP, 2012. http://dx.doi.org/10.1063/1.4765614.

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4

Pettit, Chris, and Philip Beran. "Reduced-order modeling for flutter prediction." In 41st Structures, Structural Dynamics, and Materials Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2000. http://dx.doi.org/10.2514/6.2000-1446.

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Raveh, Daniella E., and Matan Argaman. "Aeroelastic System Identification and Flutter Prediction." In 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-1440.

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Li, Wu, Karl Geiselhart, and Jay Robinson. "Flutter Prediction for Aircraft Conceptual Design." In AIAA Scitech 2019 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2019. http://dx.doi.org/10.2514/6.2019-0174.

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Zhou, Daheng, and Li Zhou. "Flutter boundary prediction based on CEEMDAN." In Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVI, edited by Peter J. Shull, Tzuyang Yu, Andrew L. Gyekenyesi, and H. Felix Wu. SPIE, 2022. http://dx.doi.org/10.1117/12.2612246.

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Zeng, Jie, P. C. Chen, and Sunil Kukreja. "Investigation of the Prediction Error Identification for Flutter Prediction." In AIAA Atmospheric Flight Mechanics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2012. http://dx.doi.org/10.2514/6.2012-4575.

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Huang, Chao, Zhigang Wu, Chao Yang, and Yuting Dai. "Flutter Boundary Prediction for a Flying-Wing Model Exhibiting Body Freedom Flutter." In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-0415.

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Melek, Merve, and Metin O. Kaya. "Supersonic flutter prediction of functionally graded panel." In 2009 4th International Conference on Recent Advances in Space Technologies (RAST). IEEE, 2009. http://dx.doi.org/10.1109/rast.2009.5158184.

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Звіти організацій з теми "Flutter Prediction"

1

Casey, J. K. Empirical Flutter Prediction Method. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada195699.

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2

Dowell, Earl H., and Kenneth C. Hall. Theoretical Prediction of Limit Cycle Oscillations in Support of Flight Flutter Testing. Fort Belvoir, VA: Defense Technical Information Center, August 2003. http://dx.doi.org/10.21236/ada426408.

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3

Farhat, Charles. Real Time Predictive Flutter Analysis and Continuous Parameter Identification of Accelerating Aircraft. Fort Belvoir, VA: Defense Technical Information Center, September 1998. http://dx.doi.org/10.21236/ada361695.

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Farhat, Charbel. Real-Time Predictive Flutter Analysis and Continuous Parameter Identification of Accelerating Aircraft. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada387498.

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5

Farhat, Charbel. Real-Time Predictive Flutter Analysis and Continuous Parameter Identification of Acclerating Aircraft. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada389378.

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