Ratneshwar Jha

Variable Stiffness Spar Approach for Aircraft Maneuver Enhancement Using ASTROS

An innovative variable stiffness spar (VSS) approach is studied for improving aircraft roll performance. In this concept some of the existing wing spars are replaced by the adaptive-structure VSS to control the stiffness as a function of Mach number and altitude. The VSS stiffness scheduling is designed to maximize the roll rate while satisfying flutter, control surface hinge moment, and maximum deflection constraints. The VSS mechanism consists of segmented spar having articulated joints at the connections with wing ribs and an electrical actuator capable of rotating the spar. The wing stiffness provided by the spar varies sinusoidally as a function of the rotation angle. The objective of the present study is to explore when and how to best apply this concept and assess its payoffs in terms of performance gains. The F/A-18 pre-roll-modification aircraft was selected as the baseline aircraft for its low torsional wing stiffness and available flight data. The multidisciplinary design optimization software ASTROS * was used tier performing the analyses in the Mach number range of M = 0.8-1.2 at altitudes up to 35,000 ft (40,668 m). Results show that VSS can amplify the aeroelastic forces and significantly enhance roll performance of aireraft.

Experimental investigation of active vibration control using neural networks and piezoelectric actuators

The use of neural networks for identification and control of smart structures is investigated experimentally. Piezoelectric actuators are employed to suppress the vibrations of a cantilevered plate subject to impulse, sine wave and band-limited white noise disturbances. The neural networks used are multilayer perceptrons trained with error backpropagation. Validation studies show that the identifier predicts the system dynamics accurately. The controller is trained adaptively with the help of the neural identifier. Experimental results demonstrate excellent closed-loop performance and robustness of the neurocontroller.

Aeroelastic tailoring using piezoelectric actuation and hybrid optimization

Active control of fixed wing aircraft using piezoelectric materials has the potential to improve its aeroelastic response while reducing weight penalties. However, the design of active aircraft wings is a complex optimization problem requiring the use of formal optimization techniques. In this paper, a hybrid optimization procedure is applied to the design of a scaled airplane wing model, represented by a flat composite plate, with piezoelectric actuation to improve the aeroelastic response. Design objectives include reduced static displacements, improved passenger comfort during gust and increased damping. Constraints are imposed on the electric power consumption and ply stresses. Design variables include composite stacking sequence, actuator/sensor locations and controller gain. Numerical results indicate significant improvements in the design objectives and physically meaningful optimal designs.

Operational modal analysis of a multi-span skew bridge using real-time wireless sensor networks

A large-scale field deployment of high-density, real-time wireless sensors networks for the acquisition of local acceleration measurements across a medium length, multi-span highway bridge is presented. The advantages, performance characteristics, and limitations of employing this emerging technology in favor of the traditional cable-based acquisition systems are discussed in the context of the in-service instrumentation and ambient vibration testing of a multi-span bridge. Of particular highlight in this study is the deployment of a large number of stationary rather than reference-based accelerometers to uniquely permit simultaneous acquisition of vibration measurements across the structure and thereby ensure consistent temperature, ambient vibration, and traffic loading. The deployment consisted of 30 dual-axis accelerometers installed across the girders of the bridge and interfaced with 30 wireless acquisition and transceiver nodes operating in two star topology networks. Real-time wireless acquisition at a per channel sampling rate of 128 samples per second was maintained across both networks for the specified test durations of 3 min with insignificant data loss. Output-only system identification of the structure from the experimental data is presented to provide estimates of natural frequencies, damping ratios, and operational mode shapes for 19 modes. The analysis of the structure under test provides a unique case study documenting the measured response of a multiple-span skewed bridge supported by elastomeric bearings. The feasibility of embedded wireless instrumentation for structural health monitoring of large civil constructions is concluded while highlighting relevant technological shortcomings and areas of further development required. In particular, previously undocumented obstacles relating to radio transmission of the sensor data using low-power 2.4 GHz wireless instrumentation, such as the effect of solid piers within the line-of-sight and the reflection of the radio waves on the surface of the water, are discussed.

An efficient analytical Bayesian method for reliability and system response updating based on Laplace and inverse first-order reliability computations

This paper presents an efficient analytical Bayesian method for reliability and system response updating without using simulations. The method includes additional information such as measurement data via Bayesian modeling to reduce estimation uncertainties. Laplace approximation method is used to evaluate Bayesian posterior distributions analytically. An efficient algorithm based on inverse first-order reliability method is developed to evaluate system responses given a reliability index or confidence interval. Since the proposed method involves no simulations such as Monte Carlo or Markov chain Monte Carlo simulations, the overall computational efficiency improves significantly, particularly for problems with complicated performance functions. A practical fatigue crack propagation problem with experimental data, and a structural scale example are presented for methodology demonstration. The accuracy and computational efficiency of the proposed method are compared with traditional simulation-based methods.

Probabilistic fatigue damage prognosis using maximum entropy approach

A general framework for probabilistic fatigue damage prognosis using maximum entropy concept is proposed and developed in this paper. The fatigue damage is calculated using a physics-based crack growth model. Due to the stochastic nature of fatigue crack propagation process, uncertainties arising from the underlying physical model, parameters of the model and the response variable measurement noise are considered and integrated into this framework. Incorporating all those uncertainties, a maximum relative entropy (MRE) approach is proposed to update the statistical description of model parameters and narrow down the prognosis deviations. A Markov Chain Monte Carlo (MCMC) simulation is then employed to generate samples from updated posterior probability distributions and provide statistical information for the maximum relative entropy updating procedure. A numerical toy problem is given to demonstrate the proposed MRE prognosis methodology. Experimental data for aluminum alloys are used to validate model predictions under uncertainty. Following this, a detailed comparison between the proposed MRE approach and the classical Bayesian updating method is performed to illustrate advantages of the proposed prognosis framework.

Neural-network-based adaptive predictive control for vibration suppression of smart structures

A neural-network-based adaptive predictive controller is developed and validated experimentally. On-line nonlinear plant identification is performed using a multilayer perceptron neural network with tapped delay inputs. The performance index includes the squared value of plant response (which is desired to be zero for vibration suppression) and a weighted squared change in the control signal. The one-step ahead prediction of plant response is used to minimize the performance index. Efficient algorithms are used for on-line plant identification and performance index minimization to achieve real-time control of plant with relatively fast response time. Piezoelectric actuators are employed to reduce the vibrations with sine wave and band-limited white noise excitation. Experimental results demonstrate the excellent performance of the developed control system. Adaptive control is verified through similar performances with changes in the plant dynamics and external excitation.

Wireless intelligent sensor and actuator network (WISAN): a scalable ultra-low-power platform for structural health monitoring

This paper presents Wireless Intelligent Sensor and Actuator Network (WISAN) as a scalable wireless platform for structural health monitoring. Design of WISAN targeted key issues arising in applications of structural health monitoring. First, scalability of system from a few sensors to hundreds of sensors is provided through hierarchical cluster-tree network architecture. Special consideration is given to reliable delivery of wireless data in real-world conditions. Second, a possibility of autonomous operation of sensor nodes from energy harvesters is ensured through extremely low power consumption in operational and standby modes of operation. Third, all the sensors and actuators operate in globally synchronized time on the order of a few microseconds through utilization of the beaconing mechanism of IEEE802.15.4 standard. Fourth, depending on application requirements, the system is capable of delivering real-time streams of sensor data or performing on-sensor storage and/or processing with result transmission. Finally, a capability to work with heterogeneous arrays of sensors and actuators is ensured by a variety of analog and digital interfaces. Results of experimental tests validate the performance of the WISAN.

Field deployment of a dense wireless sensor network for condition assessment of a bridge superstructure

With the increased demand placed on aging infrastructure, there is great interest in new condition assessment tools for bridges. The routine deterioration that bridges undergo causes a loss in the intended performance that, if undetected or unattended, can eventually lead to structural failure. Currently the primary method of bridge condition assessment involves a qualitative bridge inspection routine based on visual observations. Discussed in this paper are methods of in-situ quantitative bridge condition assessment using a dense wireless sensor array. At the core of the wireless system is an integrated network which collects data from a variety of sensors in real-time and provides analysis, assessment and decision-making tools. The advanced wireless sensor system, developed at Clarkson University for diagnostic bridge monitoring, provides independent conditioning for both accelerometers and strain transducers with high-rate wireless data transmission in a large-scale sensor network. Results from a field deployment of a dense wireless sensor network on a bridge located in New York State are presented. The field deployment and testing aid to quantify the current bridge response as well as demonstrate the ability of the system to perform bridge monitoring and condition assessment.

Crack Propagation in Prestressed Concrete Noncylinder Pipe Using Finite Element Method

Cracks are the most recognized indication of damage in Prestressed Concrete Noncylinder Pipe (NCP) due to corrosion, hydrogen embrittlement, overloading and pipes defects. In this paper, an extended form of the finite element method (XFEM) is used to study crack initiation, growth and life prediction analysis of NCP. Using XFEM enables to model the pipe without explicitly meshing the crack surfaces, and hence crack growth simulations can be carried out without the need for remeshing. For structural monitoring, stresses and strains of the damaged NCP are evaluated. The crack propagation of a damaged NCP is investigated due to increasing the internal pressure of the pipe. The proposed model is applied to obtain the location and minimum internal pressure associated with visible cracking in the pipe. Based upon the obtained results, suggestions for future work are presented and discussed.