Showing papers by "Ranjan Ganguli published in 2004"

Survey of Recent Developments in Rotorcraft Design Optimization

Abstract: OTORCRAFT engineering is highly interdisciplinary because the flexibility of the main rotor blades couples with the aerodynamics, dynamics, and control system. In addition, interaction between the rotor and fuselage further complicates helicopter system predictions. The multidisciplinary nature of the helicopter engineering problems has led researchers to investigate formal optimization methods for the design process. Applications of formal optimization methods for helicopter problems started in the early 1980s. Miura 1 provided a review of some of the early work on application of numerical optimization to helicopters. Friedmann, 2 Adelman and Mantay, 3 and Celi 4 provide further reviews of helicopter optimization. Sobieszczanski-Sobieski and Haftka 5 give a review of recent developments in multidisciplinary design optimization for aerospace problems and Gieseng and Barthelemy 6 provide an industrial perspective of multidisciplinary optimization research. Whereas considerable studies have been conducted on helicopter design optimization, several issues have prevented it from becoming as popular or successful as structural optimization. Many finite element-based design packages today have builtin optimization capacity. However, the predictive capacity of even the most sophisticated helicopter aeroelastic analysis codes remains quite poor, as evidenced in a recent study by Hansford and Vorwald, 7 where hub load predictions from several codes are compared with flight-test data. In addition, because of the nature of helicopter problems, comprehensive aeroelastic codes are highly multidisciplinary and very difficult to understand and alter except by domain experts. This is because of the complexity of the physical modeling. For example, as the blade moves over one revolution, it encounters transonic flow, reverse flow, stall, and unsteady effects including dynamic stall. Large azimuthal variations in lift result from changes in dynamic pressure

Identification of crack location and depth in a cantilever beam using a modular neural network approach

Abstract: In this paper, the flexural vibration in a cantilever beam having a transverse surface crack is considered. The modal frequency parameters are analytically computed for various crack locations and depths using a fracture mechanics based crack model. These computed modal frequencies are used to train a neural network to identify both the crack location and depth. The sensitivity of the modal frequencies to a crack increases when the crack is near the root and decreases as the crack moves to the free end of the cantilever beam. Because of the sensitive nature of this problem, a modular neural network approach is used. First, the crack location is identified with computed modal frequency parameters. Next, the crack depth is identified with computed modal frequency parameters and the identified crack location. A comparative study is made using the modular neural network architecture with two widely used neural networks, namely the multi-layer perceptron network and the radial basis function network. The proposed modular neural network method with a radial basis function network is found to perform better than the multi-layer perceptron network. In addition, the radial basis function network takes less computational time to train the network than the multi-layer perceptron network. This modular neural network architecture can be used as a non-destructive procedure for health monitoring of structures.

Helicopter vibration reduction in forward flight with induced-shear based piezoceramic actuation

Abstract: Governing equations are obtained for helicopter rotor blades with surface bonded piezoceramic actuators using Hamilton's principle. The equations are then solved for dynamic response using finite element discretization in the spatial and time domains. A time domain unsteady aerodynamic model is used to obtain the airloads. The nonlinear relationship between the piezoelectric shear coefficient and applied ac field is represented as a polynomial curve fit. The nonlinear effects are investigated by applying a sinusoidal voltage to the helicopter rotor blade. The rotor blade is modeled as a two-cell box section with piezoelectric layers surface bonded to the top and bottom of the box beam. Comparison of results with linear and nonlinear shear coefficients is presented. Use of a nonlinear relationship (compared to linear) to achieve targeted reductions in strains or displacements results in a reduction in the requirement of applied amplitude of the sinusoidal field. A rate feedback control law is implemented which feeds back the higher harmonics of the time rate of change of strain in the azimuthal direction. The sensed voltage is then applied to the rotor blade, resulting in a vibration reduction of approximately 43% for a four-bladed, soft-in-plane hingeless rotor in forward flight.

Adaptive Myriad Filter for Improved Gas Turbine Condition Monitoring Using Transient Data

Abstract: The removal of noise and outliers from measurement signals is a major problem in jet engine health monitoring. In this study, we look at the myriad filter as a substitute for the moving average filter that is widely used in the gas turbine industry. The three ideal test signals used in this study are the step signal that simulates a single fault in the gas turbine, while ramp and quadratic signals simulate long term deterioration. Results show that the myriad filter performs better in noise reduction and outlier removal when compared to the moving average filter. Further, an adaptive weighted myriad filter algorithm that adapts to the quality of incoming data is studied. The filters are demonstrated on simulated clean and deteriorated engine data obtained from an acceleration process from idle to maximum thrust condition. This data was obtained from published literature and was simulated using a transient performance prediction code. The deteriorated engine had single component faults in the low pressure turbine and intermediate pressure compressor. The signals are obtained from T2 (IPC total outlet temperature) and T6 (LPT total outlet temperature) engine sensors with their nonrepeatability values that were used as noise levels. The weighted myriad filter shows even greater noise reduction and outlier removal when compared to the sample myriad and a FIR filter in the gas turbine diagnosis. Adaptive filters such as those considered in this study are also useful for online health monitoring, as they can adapt to changes in quality of incoming data.

Trend Shift Detection in Jet Engine Gas Path Measurements Using Cascaded Recursive Median Filter With Gradient and Laplacian Edge Detector

Abstract: Trend shift detection is posed as a two-part problem: filtering of the gas turbine measurement deltas followed by the use of edge detection algorithms. Measurement deltas are deviations in engine gas path measurements from a "good" baseline engine and are a key, health signal used for gas turbine performance diagnostics. The measurements used in this study are exhaust gas temperature, low rotor speed, high rotor speed and fuel flow, which are called cockpit measurements and are typically found on most commercial jet engines. In this study, a cascaded recursive median (RM) filter of increasing order is used for the purpose of noise reduction and outlier removal, and a hybrid edge detector that uses both gradient and Laplacian of the cascaded RM filtered signal are used for the detection of step change in the measurements. Simulated results with test signals indicate that cascaded RM filters can give a noise reduction of more than 38% while preserving the essential features of the signal. The cascaded RM filter also shows excellent robustness in dealing with outliers, which are quite often found in gas turbine data, and call cause spurious trend detections. Suitable thresholding of the gradient edge detector coupled with the use of the Laplacian edge detector for cross checking can reduce the system false alarms and missed detection rate. Further reduction in the trend shift detection false alarm and missed detection rate can be achieved by selecting gas path measurements with higher signal-to-noise ratios.

Soft Computing Application for Gas Path Fault Isolation

Abstract: A fuzzy system that automatically develops its rule base from a linearized performance model of the engine by selecting the membership functions and number of fuzzy sets is developed in this study to perform gas turbine fault isolation The faults modeled are module faults in five modules: fan, low pressure compressor, high pressure compressor, high pressure turbine and low pressure turbine The measurements used are deviations in exhaust gas temperature, low rotor speed, high rotor speed and fuel flow from a base line ‘good engine’ A genetic algorithm is used to tune the fuzzy sets to maximize fault isolation success rate A novel scheme is developed which optimizes the fuzzy system using very few design variables and therefore is computationally efficient Results with simulated data show that genetic fuzzy system isolates faults with accuracy greater than that of a manually developed fuzzy system developed by the authors Furthermore, the genetic fuzzy system allows rapid development of the rule base if the fault signatures and measurement uncertainties change In addition, the genetic fuzzy system reduces the human effort needed in the trial and error process used to design the fuzzy system and makes the development of such a system easier and faster A radial basis neural network is also used to preprocess the measurements before fault isolation The radial basis network shows significant noise reduction and when combined with the genetic fuzzy system leads to a diagnostic system that is highly robust to the presence of noise in dataCopyright © 2004 by ASME

Validation of Comprehensive Helicopter Aeroelastic Analysis with Experimental Data

Abstract: The experimental data for a 4-bladed soft-inplane hingeless main rotor is used to validate a comprehensive aeroelastic analysis. A finite element model has been developed for the rotor blade which predicts rotating frequencies quite well, across a range of rotation speeds. The helicopter is trimmed and the predicted trim-control angles are found to be in the range of measured values for a variety of flight speeds. Power predictions over a range of forward speeds also compare well. Finally, the aeroelastic analysis is used to study the importance of aerodynamic models on the vibration prediction. Unsteady aerodynamics and free-wake models have been investigated.

Soft computing application for gas path performance diagnostics

Abstract: A fuzzy system that automatically develops its rule base from a linearized performance model of the engine by selecting the membership functions and number of fuzzy sets is developed in this study to perform gas turbine fault isolation. The faults modeled are module faults in five modules: fan, low pressure compressor, high pressure compressor, high pressure turbine and low pressure turbine. The measurements used are deviations in exhaust gas temperature, low rotor speed, high rotor speed and fuel flow from a base line ‘good engine’. A genetic algorithm is used to tune the fuzzy sets to maximize fault isolation success rate. A novel scheme is developed which optimizes the fuzzy system using very few design variables and therefore is computationally efficient. Results with simulated data show that genetic fuzzy system isolates faults with accuracy greater than that of a manually developed fuzzy system. Furthermore, the genetic fuzzy system allows rapid development of the rule base if the fault signatures and measurement uncertainties change. In addition, the genetic fuzzy system reduces the human effort needed in the trial and error process used to design the fuzzy system. A radial basis neural network is also used to preprocess the measurements before fault isolation. The radial basis network shows significant noise reduction and when combined with the genetic fuzzy system leads to a diagnostic system that is highly robust to the presence of noise in data.