A Study of The Fatigue, Impact and fracture-Toughness Characteristics of Electroslag Refined IS-7670 Alloy

The nuclear, aerospace, naval and missile industries place emphasis on materials with high structural integrity and reliable performance so as to meet certain stringent requirements in service. Strength is not the only criterion for selection. Properties such as fatigue resistance. impact toughness and fracture toughness are equally important. Electroslag refining (ESR) has been used widely and successfully over the years for improving the fatigue resistance, creep resistance, impact strength and fracture toughness of steels and alloy steels. But application of ESR to aluminium alloys is only a recent endeavour. A high-strength aircraft aluminium alloy IS: 7670 was therefore chosen for studies on the fatigue strength and the impact and fracture toughness. The results indicate that the fatigue resistance is considerably improved after refining and that the impact strength and fracture toughness of the refined alloy are comparable with that of the unrefined alloy.

Prediction of load-deflection behaviour using fracture energy GF: A comparative study of two fracture models for concrete

Load-deflection curves for a notched beam under three-point load are determined using the Fictitious Crack Model (FCM) and Blunt Crack Model (BCM). Two values of fracture energy GF are used in this analysis: (i) GF obtained from the size effect law and (ii) GF obtained independently of the size effect. The predicted load-deflection diagrams are compared with the experimental ones obtained for the beams tested by Jenq and Shah. In addition, the values of maximum load (Pmax) obtained by the analyses are compared with the experimental ones for beams tested by Jenq and Shah and by Bažant and Pfeiffer. The results indicate that the descending portion of the load-deflection curve is very sensitive to the GF value used.

Fracture Properties of Concrete-Concrete Interfaces Using Digital Image Correlation

The mode I and mode II fracture toughness and the critical strain energy release rate for different concrete-concrete jointed interfaces are experimentally determined using the Digital Image Correlation technique. Concrete beams having different compressive strength materials on either side of a centrally placed vertical interface are prepared and tested under three-point bending in a closed loop servo-controlled testing machine under crack mouth opening displacement control. Digital images are captured before loading (undeformed state) and at different instances of loading. These images are analyzed using correlation techniques to compute the surface displacements, strain components, crack opening and sliding displacements, load-point displacement, crack length and crack tip location. It is seen that the CMOD and vertical load-point displacement computed using DIC analysis matches well with those measured experimentally.

A numerical investigation of ductile fracture initiation in a high-strength low-alloy steel

In this work, static and drop-weight impact experiments, which have been conducted using three-point bend fracture specimens of a high-strength low-alloy steel, are analysed by performing finite-element simulations. The Gurson constitutive model that accounts for the ductile failure mechanisms of microvoid nucleation, growth and is employed within the framework of a finite deformation plasticity theory. Two populations of second-phase particles are considered, including large inclusions which initiate voids at an early stage and small particles which require large strains to nucleate voids. The most important objective of the work is to assess quantitatively the effects of material inertia, strain rate sensitivity and local adiabatic temperature rise (due to conversion of plastic work into heat) on dynamic ductile crack initiation. This is accomplished by comparing the evolution histories of void volume fraction near the notch tip in the static analysis with the dynamic analyses. The results indicate that increased strain hardening caused by strain rate sensitivity, which becomes important under dynamic loading, plays a benign role in considerably slowing down the void growth rate near the notch tip. This is partially opposed by thermal softening caused by adiabatic heating near the notch tip.

High-temperature low-cycle fatigue behavior of a NIMONIC PE-16 superalloy—Correlation with deformation and fracture

Low-cycle fatigue (LCF) responses of NIMONIC PE-16 for various prior microstructures and strain amplitudes have been evaluated and the fatigue behavior has been explained in terms of the operative deformation mechanisms. Total strain-controlled LCF tests were performed at 923 K on samples possessing three different prior microstructures: alloy A in solution-annealed condition (free of γ′ and carbides), alloy B with double aging treatment (spherical γ′ of 18-nm diameter and M23C6), and alloy C with another double aging treatment (γ′ of size 35 nm, MC and M23C6). All three microstructures exhibited an intial cyclic hardening followed by a period of gradual softening at 923 K. Coffin-Manson plots describing the plastic strain amplitudevs number of reversals to failure showed that alloy A had maximum fatigue life while C showed the least. Alloy B exhibited a two-slope behavior in the Coffin-Manson plot over the strain amplitudes investigated. This has been ascribed to the change in the degree of homogeneity of deformation at high and low strain amplitudes. Transmission electron microscopic studies were carried out to characterize the various deformation mechanisms and precipitation reactions occurring during fatigue testign. Fresh precipitation of fine γ′ was confirmed by the development of “mottled contrast” in alloy C. Evidence for the shearing of the ordered γ′ precipitates was revealed by the presence of superdislocations in alloy C. Repeated shearing during cyclic loading led to the reduction in the size of the γ′ and consequent softening. Coarser γ′ precipitates were associated with Orowan loops. The observed fatigue behavior has been rationalized based on the micromechanisms stated above and on the degree of homogenization of slip assessed by slipband spacing measurements on tested samples.

Influence of grain size on high temperature fracture in a Mg AZ31 alloy

Tensile experiments at 673 K and grain sizes from similar to 8 to 17 mu m revealed large ductility at a low strain rate and a reduced ductility at a high strain rate, corresponding to a change from a high to a low value for the strain rate sensitivity. High strain rate deformation led to fracture by flow localization, whereas low strain rate deformation involved fracture by cavity nucleation and growth. Analysis revealed that grain boundary migration can assist significantly in reducing the stress concentrations caused by grain boundary sliding, thereby retarding cavity nucleation. Calculations demonstrate that the interlinkage of voids parallel and perpendicular to the tensile axis occurs significantly, so that it is not always possible to use the cavity shapes to distinguish between diffusion and plasticity controlled growth. Cavitation damage evolves slowly in materials with a coarser grain size because of reduced nucleation related to a reduction in the strain rate sensitivity and associated grain boundary sliding. (C) 2011 Elsevier B.V. All rights reserved.

An experimental study on acoustic emission energy as a quantitative measure of size independent specific fracture energy of concrete beams

Notched three point bend specimens (TPB) were tested under crack mouth opening displacement (CMOD) control at a rate of 0.0004 mm/s and during the fracture process acoustic emissions (AE) were simultaneously monitored. It was observed that AE energy could be related to fracture energy. An experimental study was done to understand the behavior of AE energy with parameters of concrete like its strength and size. In this study, AE energy was used as a quantitative measure of size independent specific fracture energy of concrete beams and the concepts of boundary effect and local fracture energy were used to obtain size independent AE energy from which size independent fracture energy was obtained. (C) 2010 Elsevier Ltd. All rights reserved.

Model Based Target Tracking in a Wireless Network of Passive Infrared Sensor Nodes

We consider the problem of tracking an intruder in a plane region by using a wireless sensor network comprising motes equipped with passive infrared (PIR) sensors deployed over the region. An input-output model for the PIR sensor and a method to estimate the angular speed of the target from the sensor output are proposed. With the measurement model so obtained, we study the centralized and decentralized tracking performance using the extended Kalman filter.

On Network-Error Correcting Convolutional Codes under the BSC Edge Error Model

Convolutional network-error correcting codes (CNECCs) are known to provide error correcting capability in acyclic instantaneous networks within the network coding paradigm under small field size conditions. In this work, we investigate the performance of CNECCs under the error model of the network where the edges are assumed to be statistically independent binary symmetric channels, each with the same probability of error pe(0 <= p(e) < 0.5). We obtain bounds on the performance of such CNECCs based on a modified generating function (the transfer function) of the CNECCs. For a given network, we derive a mathematical condition on how small p(e) should be so that only single edge network-errors need to be accounted for, thus reducing the complexity of evaluating the probability of error of any CNECC. Simulations indicate that convolutional codes are required to possess different properties to achieve good performance in low p(e) and high p(e) regimes. For the low p(e) regime, convolutional codes with good distance properties show good performance. For the high p(e) regime, convolutional codes that have a good slope ( the minimum normalized cycle weight) are seen to be good. We derive a lower bound on the slope of any rate b/c convolutional code with a certain degree.

Flow Instabilities and fracture in Ti-6Al-4V deformed in compression at 298 K to 673 K

Uniaxial compression tests were conducted on Ti-6Al-4V specimens in the strain-rate range df 0.001 to 1 s(-1) and temperature range of 298 to 673 K. The stress-strain curves exhibited a peak flow stress followed by flow softening. Up to 523 K, the specimens cracked catastrophically after the flow softening started. Adiabatic shear banding was observed in this regime. The fracture surface exhibited both mode I and II fracture features. The state of stress existing in a compression test specimen when bulging occurs is responsible for this fracture. The instabilities observed in the present tests are classified as ''geometric'' in nature and are state-of-stress dependant, unlike the ''intrinsic'' instabilities, which are dependant on the dynamic constitutive behavior of the material.

Fracture Analysis of Concrete Structural Components Accounting for Tension Softening Effect

This paper presents methodologies for fracture analysis of concrete structural components with and without considering tension softening effect. Stress intensity factor (SIF) is computed by using analytical approach and finite element analysis. In the analytical approach, SW accounting for tension softening effect has been obtained as the difference of SIP obtained using linear elastic fracture mechanics (LEFM) principles and SIP due to closing pressure. Superposition principle has been used by accounting for non-linearity in incremental form. SW due to crack closing force applied on the effective crack face inside the process zone has been computed using Green's function approach. In finite element analysis, the domain integral method has been used for computation of SIR The domain integral method is used to calculate the strain energy release rate and SIF when a crack grows. Numerical studies have been conducted on notched 3-point bending concrete specimen with and without considering the cohesive stresses. It is observed from the studies that SW obtained from the finite element analysis with and without considering the cohesive stresses is in good agreement with the corresponding analytical value. The effect of cohesive stress on SW decreases with increase of crack length. Further, studies have been conducted on geometrically similar structures and observed that (i) the effect of cohesive stress on SW is significant with increase of load for a particular crack length and (iii) SW values decreases with increase of tensile strength for a particular crack length and load.

A single network adaptive critic (SNAC) architecture for optimal control synthesis for a class of nonlinear systems

Even though dynamic programming offers an optimal control solution in a state feedback form, the method is overwhelmed by computational and storage requirements. Approximate dynamic programming implemented with an Adaptive Critic (AC) neural network structure has evolved as a powerful alternative technique that obviates the need for excessive computations and storage requirements in solving optimal control problems. In this paper, an improvement to the AC architecture, called the �Single Network Adaptive Critic (SNAC)� is presented. This approach is applicable to a wide class of nonlinear systems where the optimal control (stationary) equation can be explicitly expressed in terms of the state and costate variables. The selection of this terminology is guided by the fact that it eliminates the use of one neural network (namely the action network) that is part of a typical dual network AC setup. As a consequence, the SNAC architecture offers three potential advantages: a simpler architecture, lesser computational load and elimination of the approximation error associated with the eliminated network. In order to demonstrate these benefits and the control synthesis technique using SNAC, two problems have been solved with the AC and SNAC approaches and their computational performances are compared. One of these problems is a real-life Micro-Electro-Mechanical-system (MEMS) problem, which demonstrates that the SNAC technique is applicable to complex engineering systems.

Adaptive optimization of continuous bioreactor using neural network model

An adaptive optimization algorithm using backpropogation neural network model for dynamic identification is developed. The algorithm is applied to maximize the cellular productivity of a continuous culture of baker's yeast. The robustness of the algorithm is demonstrated in determining and maintaining the optimal dilution rate of the continuous bioreactor in presence of disturbances in environmental conditions and microbial culture characteristics. The simulation results show that a significant reduction in time required to reach optimal operating levels can be achieved using neural network model compared with the traditional dynamic linear input-output model. The extension of the algorithm for multivariable adaptive optimization of continuous bioreactor is briefly discussed.

Optimal management of beaver population using a reduced-order distributed parameter model and single network adaptive critics

Beavers are often found to be in conflict with human interests by creating nuisances like building dams on flowing water (leading to flooding), blocking irrigation canals, cutting down timbers, etc. At the same time they contribute to raising water tables, increased vegetation, etc. Consequently, maintaining an optimal beaver population is beneficial. Because of their diffusion externality (due to migratory nature), strategies based on lumped parameter models are often ineffective. Using a distributed parameter model for beaver population that accounts for their spatial and temporal behavior, an optimal control (trapping) strategy is presented in this paper that leads to a desired distribution of the animal density in a region in the long run. The optimal control solution presented, imbeds the solution for a large number of initial conditions (i.e., it has a feedback form), which is otherwise nontrivial to obtain. The solution obtained can be used in real-time by a nonexpert in control theory since it involves only using the neural networks trained offline. Proper orthogonal decomposition-based basis function design followed by their use in a Galerkin projection has been incorporated in the solution process as a model reduction technique. Optimal solutions are obtained through a "single network adaptive critic" (SNAC) neural-network architecture.

A novel neural network design for long range prediction of rainfall pattern

The importance of long-range prediction of rainfall pattern for devising and planning agricultural strategies cannot be overemphasized. However, the prediction of rainfall pattern remains a difficult problem and the desired level of accuracy has not been reached. The conventional methods for prediction of rainfall use either dynamical or statistical modelling. In this article we report the results of a new modelling technique using artificial neural networks. Artificial neural networks are especially useful where the dynamical processes and their interrelations for a given phenomenon are not known with sufficient accuracy. Since conventional neural networks were found to be unsuitable for simulating and predicting rainfall patterns, a generalized structure of a neural network was then explored and found to provide consistent prediction (hindcast) of all-India annual mean rainfall with good accuracy. Performance and consistency of this network are evaluated and compared with those of other (conventional) neural networks. It is shown that the generalized network can make consistently good prediction of annual mean rainfall. Immediate application and potential of such a prediction system are discussed.