Embedded PZT wafer sensors for structural health monitoring

Recent advances in structural integrity evaluation have led to the development. of PZT wafer sensors (PWAS) which can be embedded or surface mounted for both acoustic emission (AE) and ultrasonic (UT) modes, which forms an integrated approach for Structural Health Monitoring (SHM) of aerospace structures. For the fabrication of PWAS wafers, soft PZT formulation (SP-5H Grade containing dopants like BA, SM, CA, ZN, Y and HF) were used. The piezoelectric charge constant (d(33)) was measured by a d(33) meter. As a first step towards the final objective of developing Health monitoring methods with embedded PWAS, experiments were conducted on aluminum and composite plates of finite dimensions using PWAS sensors. The AE source was simulated by breaking 0.5mm pencil lead on the surface of a thin plate. Experiments were also conducted with surface mounted PZT films and conventional AE sensors in order to establish the sensitivity of PWAS. A comparison of results of theoretical and experimental work shows good agreement.

Parametric analysis of acoustic emission signals for evaluating damage in composites using a PVDF film sensor

With the increased utilization of advanced composites in strategic industries, the concept of Structural Health Monitoring (SHM) with its inherent advantages is gaining ground over the conventional methods of NDE and NDI. The most attractive feature of this concept is on-line evaluation using embedded sensors. Consequently, development of methodologies with identification of appropriate sensors such as PVDF films becomes the key for exploiting the new concept. And, of the methods used for on-line evaluation acoustic emission has been most effective. Thus, Acoustic Emission (AE) generated during static tensile loading of glass fiber reinforced plastic composites was monitored using a Polyvinylidene fluoride (PVDF) film sensor. The frequency response of the film sensor was obtained with pencil lead breakage tests to choose the appropriate band of operation. The specimen considered for the experiments were chosen to characterize the differences in the operation of the failure mechanisms through AE parametric analysis. The results of the investigations can be characterized using AE parameter indicating that a PVDF film sensor was effective as an AE sensor used in structural health monitoring on-line.

Investigations of the scaling criteria for a mild combustion burner

In this paper, a new strategy for scaling burners based on "mild combustion" is evolved and adopted to scaling a burner from 3 to a 150 kW burner at a high heat release Late of 5 MW/m(3) Existing scaling methods (constant velocity, constant residence time, and Cole's procedure [Proc. Combust. Inst. 28 (2000) 1297]) are found to be inadequate for mild combustion burners. Constant velocity approach leads to reduced heat release rates at large sizes and constant residence time approach in unacceptable levels of pressure drop across the system. To achieve mild combustion at high heat release rates at all scales, a modified approach with high recirculation is adopted in the present studies. Major geometrical dimensions are scaled as D similar to Q(1/3) with an air injection velocity of similar to 100 m/s (Delta p similar to 600 mm water gauge). Using CFD support, the position of air injection holes is selected to enhance the recirculation rates. The precise role of secondary air is to increase the recirculation rates and burn LIP the residual CO in the downstream. Measurements of temperature and oxidizer concentrations inside 3 kW, 150 kW burner and a jet flame are used to distinguish the combustion process in these burners. The burner can be used for a wide range of fuels from LPG to producer gas as extremes. Up to 8 dB of noise level reduction is observed in comparison to the conventional combustion mode. Exhaust NO emissions below 26 and 3 ppm and temperatures 1710 and 1520 K were measured for LPG and producer gas when the burner is operated at stoichiometry. (c) 2004 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Health monitoring,of a helicopter rotor in forward flight using fuzzy logic

A fuzzy logic system is developed for helicopter rotor system fault isolation. Inputs to the fuzzy logic system are measurement deviations of blade bending and torsion response and vibration from a "good" undamaged helicopter rotor. The rotor system measurements used are flap and lag bending tip deflections, elastic twist deflection at the tip, and three forces and three moments at the rotor hub. The fuzzy logic system uses rules developed from an aeroelastic model of the helicopter rotor with implanted faults to isolate the fault while accounting for uncertainty in the measurements. The faults modeled include moisture absorption, loss of trim mass, damaged lag damper, damaged pitch control system, misadjusted pitch link, and damaged flap. Tests with simulated data show that the fuzzy system isolates rotor system faults with an accuracy of about 90-100%. Furthermore, the fuzzy system is robust and gives excellent results, even when some measurements are not available. A rule-based expert system based on similar rules from the aeroelastic model performs much more poorly than the fuzzy system in the presence of high levels of uncertainty.

Noise and outlier removal from jet engine health signals using weighted FIR median hybrid filters

The removal of noise and outliers from measurement signals is a major problem in jet engine health monitoring. Topical measurement signals found in most jet engines include low rotor speed, high rotor speed. fuel flow and exhaust gas temperature. Deviations in these measurements from a baseline 'good' engine are often called measurement deltas and the health signals used for fault detection, isolation, trending and data mining. Linear filters such as the FIR moving average filter and IIR exponential average filter are used in the industry to remove noise and outliers from the jet engine measurement deltas. However, the use of linear filters can lead to loss of critical features in the signal that can contain information about maintenance and repair events that could be used by fault isolation algorithms to determine engine condition or by data mining algorithms to learn valuable patterns in the data, Non-linear filters such as the median and weighted median hybrid filters offer the opportunity to remove noise and gross outliers from signals while preserving features. In this study. a comparison of traditional linear filters popular in the jet engine industry is made with the median filter and the subfilter weighted FIR median hybrid (SWFMH) filter. Results using simulated data with implanted faults shows that the SWFMH filter results in a noise reduction of over 60 per cent compared to only 20 per cent for FIR filters and 30 per cent for IIR filters. Preprocessing jet engine health signals using the SWFMH filter would greatly improve the accuracy of diagnostic systems. (C) 2002 Published by Elsevier Science Ltd.

Performance evaluation of nonthermal plasma reactors for NO oxidation in diesel engine exhaust gas treatment

The discharge plasma-chemical hybrid process for NOinfinity removal from the flue gas emissions is an extremely effective and economical approach in comparison with the conventional selective catalytic reduction system. In this paper we bring out a relative comparison of several discharge plasma reactors from the point of NO removal efficiency. The reactors were either energized by ac or by repetitive pulses. Ferroelectric pellets were used to study the effect of pellet assisted discharges on gas cleaning. Diesel engine exhaust, at different loads; is used to approximately simulate the flue gas composition. Investigations were carried out at room temperature with respect to the variation of reaction products against the discharge power. Main emphasis is laid on the oxidation of NO to NO2, without reducing NOx concentration (i.e., minimum reaction byproducts), with least power consumption. The produced NO2 will be totally converted to N-2 and Na-2 SO4 using Na-2 SO3. The ac packed-bed reactor and pelletless pulsed corona reactor showed better performance, with minimum reaction products for a given power, when the NO concentration was low (similar to 100 ppm). When the engine load exceeds 50% (NO > 300 ppm) there was not much decrease in NO reduction and more or less all the reactors performed equally. The total operating cost of the plasma-chemical hybrid system becomes $4010/ton of NO, which is 1/3-1/5 of the conventional selective catalytic process.

Flow-acoustic Characterisation of a Cavity-based Combustor Configuration

This study concerns the flow-acoustic characterisation of a cavity-based combustor configuration. A well-validated numerical tool has been used to simulate the unsteady, two-dimensional reacting flow. Initially, a conventional flow over a cavity with dimensions and conditions corresponding to a compact cavity combustor was studied. Cavity mass injections in the form of fuel and air injections required for trapped vortex formation were then employed and the resonance features of this configuration were studied. The results indicate that the cavity depth mode resonance mechanism is dominant at the conditions studied in this work and that the oscillation frequencies do not change with cavity air injection. This observation is important since it implies that the only important variable which can alter resonant frequencies is the cavity depth. With combustion, the pressure oscillation amplitude was observed to increases significantly due to periodic entrainment of the cavity air jet and fluctuation of fuel-air mixture composition to produce highly fluctuating heat-release rates. The underlying mechanisms of the unsteady flow in the cavity combustor identified in this study indicate the strong dependence of the acoustics on the cavity injection strategies.

Post combustion in cupola for pollution control

Carbon monoxide, a major pollutant from the cupola, is poisonous and flammable. It can vary from 12 to 25% in cupola emissions. Carbon monoxide content in cupola emissions can be reduced by the post-combustion air input at the appropriate level into the stack. Scientific support to this has been provided by simulation of the combustion process in the cupola. Location and the extent of input of air for post combustion into the stack have been determined.

Performance evaluation of nonthermal plasma reactors for NO oxidation in diesel engine exhaust gas treatment

The discharge plasma-chemical hybrid process for NO/sub x/ removal from the due gas emissions is an extremely effective and economical approach in comparison with the conventional selective catalytic reduction system. In this paper we bring out a relative comparison of several discharge plasma reactors from the point of NO removal efficiency. The reactors were either energized by AC or by repetitive pulses. Ferroelectric pellets were used to study the effect of pellet assisted discharges on gas cleaning. Diesel engine exhaust, at different loads, is used to approximately simulate the due gas composition. Investigations were carried out at room temperature with respect to the variation of reaction products against the discharge power. Main emphasis is laid on the oxidation of NO to NO/sub 2/, without reducing NOx concentration (i.e., minimum reaction byproducts), with least power consumption. The produced NO/sub 2/ will be totally converted to N/sub 2/ and Na/sub 2/SO/sub 4/ using Na/sub 2/SO/sub 3/. The AC packed bed reactor and pelletless pulsed corona reactor showed better performance, with minimum reaction products for a given power, when the NO concentration was low (/spl sim/100 ppm). At high engine loads (NO>300 ppm) there was not much decrease in NO/sub x/ reduction and more or less all the reactors performed equally. The paper discusses these observations in detail.

3-D acoustic analysis of elliptical chamber mufflers having an end-inlet and a side-outlet: An impedance matrix approach

The acoustical behavior of an elliptical chamber muffler having an end-inlet and side-outlet port is analyzed semi-analytically. A uniform piston source is assumed to model the 3-D acoustic field in the elliptical chamber cavity. Towards this end, we consider the modal expansion of acoustic pressure field in the elliptical cavity in terms of angular and radial Mathieu functions, subjected to rigid wall condition, whereupon under the assumption of a point source, Green's function is obtained. On integrating this function over piston area of the side or end port and dividing it by piston area, one obtains the acoustic field, whence one can find the impedance matrix parameters characterizing the 2-port system. The acoustic performance of these configurations is evaluated in terms of transmission loss (TL). The analytical results thus obtained are compared with 3-D HA carried on a commercial software for certain muffler configurations. These show excellent agreement, thereby validating the 3-D semi-analytical piston driven model. The influence of the chamber length as well as the angular and axial location of the end and side ports on TL performance is also discussed, thus providing useful guidelines to the muffler designer. (c) 2011 Elsevier B.V. All rights reserved.

Full Chemical Kinetic Simulation of Biogas Early Phase Combustion in SI Engines

This paper presents computational work on the biogas early phase combustion in spark ignition (SI) engines using detailed chemical kinetics. Specifically, the early phase combustion is studied to assess the effect of various ignition parameters such as spark plug location, spark energy, and number of spark plugs. An integrated version of the KIVA-3V and CHEMKIN codes was developed and used for the simulations utilizing detailed kinetics involving 325 reactions and 53 species The results show that location of the spark plug and local flow field play an important role. A central plug configuration, which is associated with higher local flow velocities in the vicinity of the spark plug, showed faster initial combustion. Although a dual plug configuration shows the highest rate of fuel consumption, it is comparable to the rate exhibited by the central plug case. The radical species important in the initiation of combustion are identified, and their concentrations are monitored during the early phase of combustion. The concentration of these radicals is also observed to correlate very well with the above-mentioned trend.Thus, the role of these radicals in promoting faster combustion has been clearly established. It is also observed that the minimum ignition energy required to initiate a self-sustained flame depends on the flow field condition in the vicinity of the spark plug.Increasing the methane content in the biogas has shown improved combustion.