Microstructural Dependence of Work Hardening Behavior in Martensite-Ferrite Microalloyed Steels

Martensite-ferrite microstructures were produced in four microalloyed steels A (Fe-0.44C-Cr-V), B (Fe-0.26C-Cr-V), C (Fe-0.34C-Cr-Ti-V), and D (Fe-0.23C-Cr-V) by intercritical annealing. SEM analysis reveals that steels A and C contained higher martensite fraction and finer ferrite when compared to steels B and D which contained coarser ferrite grains and lower martensite fraction. A network of martensite phase surrounding the ferrite grains was found in all the steels. Crystallographic texture was very weak in these steels as indicated by EBSD analysis. The steels contained negligible volume fraction of retained austenite (approx. 3-6%). TEM analysis revealed the presence of twinned and lath martensite in these steels along with ferrite. Precipitates (carbides and nitrides) of Ti and V of various shapes with few nanometers size were found, particularly in the microstructures of steel B. Work hardening behavior of these steels at ambient temperature was evaluated through modified Jaoul-Crussard analysis, and it was characterized by two stages due to presence of martensite and ferrite phases in their microstructure. Steel A displayed large work hardening among other steel compositions. Work hardening behavior of the steels at a warm working temperature of 540 A degrees C was characterized by a single stage due to the decomposition of martensite into ferrite and carbides at this temperature as indicated by SEM images of the steels after warm deformation.

Adaptive mesh generation for singularly perturbed fourth-order ordinary differential equations

In this paper, we consider a singularly perturbed boundary-value problem for fourth-order ordinary differential equation (ODE) whose highest-order derivative is multiplied by a small perturbation parameter. To solve this ODE, we transform the differential equation into a coupled system of two singularly perturbed ODEs. The classical central difference scheme is used to discretize the system of ODEs on a nonuniform mesh which is generated by equidistribution of a positive monitor function. We have shown that the proposed technique provides first-order accuracy independent of the perturbation parameter. Numerical experiments are provided to validate the theoretical results.

Synergistic Effect of Mo plus Cu Codoping on the Photocatalytic Behavior of Metastable TiO2 Solid Solutions

Codoping with Cu and Mo is shown to have a synergistic effect on the photocatalytic activity of TiO2. The enhancement in activity is observed only if the synthesis route results in TiO2 in which (Cu, Mo) codopants are forced into the TiO2 lattice. Using X-ray photoelectron spectroscopy, Cu and Mo are shown to be present in the +2 and +6 oxidation states, respectively. A systematic study of the ternary system shows that TiO2 containing 6 mol % CuO and 1.5 mol % MoO3 is the most active ternary composition. Ab initio calculations show that codoping of TiO2 using (Mo, Cu) introduces levels above the valence band, and below the conduction band, resulting in a significant reduction in the band gap (similar to 0.8 eV). However, codoping also introduces deep defect states, which can have a deleterious impact on photoactivity. This helps rationalize the narrow compositional window over which the enhancement in photocatalytic activity is observed.

Optimal control analysis of the dynamic growth behavior of microorganisms

Understanding the growth behavior of microorganisms using modeling and optimization techniques is an active area of research in the fields of biochemical engineering and systems biology. In this paper, we propose a general modeling framework, based on Monad model, to model the growth of microorganisms. Utilizing the general framework, we formulate an optimal control problem with the objective of maximizing a long-term cellular goal and solve it analytically under various constraints for the growth of microorganisms in a two substrate batch environment. We investigate the relation between long term and short term cellular goals and show that the objective of maximizing cellular concentration at a fixed final time is equivalent to maximization of instantaneous growth rate. We then establish the mathematical connection between the generalized framework and optimal and cybernetic modeling frameworks and derive generalized governing dynamic equations for optimal and cybernetic models. We finally illustrate the influence of various constraints in the cybernetic modeling framework on the optimal growth behavior of microorganisms by solving several dynamic optimization problems using genetic algorithms. (C) 2014 Published by Elsevier Inc.

Adaptive Flight-Control Design Using Neural-Network-Aided Optimal Nonlinear Dynamic Inversion

A neural-network-aided nonlinear dynamic inversion-based hybrid technique of model reference adaptive control flight-control system design is presented in this paper. Here, the gains of the nonlinear dynamic inversion-based flight-control system are dynamically selected in such a manner that the resulting controller mimics a single network, adaptive control, optimal nonlinear controller for state regulation. Traditional model reference adaptive control methods use a linearized reference model, and the presented control design method employs a nonlinear reference model to compute the nonlinear dynamic inversion gains. This innovation of designing the gain elements after synthesizing the single network adaptive controller maintains the advantages that an optimal controller offers, yet it retains a simple closed-form control expression in state feedback form, which can easily be modified for tracking problems without demanding any a priori knowledge of the reference signals. The strength of the technique is demonstrated by considering the longitudinal motion of a nonlinear aircraft system. An extended single network adaptive control/nonlinear dynamic inversion adaptive control design architecture is also presented, which adapts online to three failure conditions, namely, a thrust failure, an elevator failure, and an inaccuracy in the estimation of C-M alpha. Simulation results demonstrate that the presented adaptive flight controller generates a near-optimal response when compared to a traditional nonlinear dynamic inversion controller.

Differential Cocrystallization Behavior of Isomeric Pyridine Carboxamides toward Antitubercular Drug Pyrazinoic Acid

Pyrazinoic acid, the active form of the antitubercular pro-drug Pyrazinamide, is an amphiprotic molecule containing carboxylic acid and pyridine groups and therefore can form both salts and cocrystals with relevant partner molecules. Cocrystallization of pyrazinoic acid with isomeric pyridine carboxamide series resulted in a dimorphic mixed-ionic complex with isonicotinamide and in eutectics with nicotinamide and picolinamide, respectively. It is observed that with alteration of the carboxamide position, steric and electrostatic compatibility issues between molecules of the combination emerge and affect intermolecular interactions and supramolecular growth, thus leading to either cocrystal or eutectic for different pyrazinoic acid-pyridine carboxamide combinations. Intermolecular interaction energy calculations have been performed to understand the role of underlying energetics on the formation of cocrystal/eutectic in different combinations. On the other hand, two molecular salts with piperazine and cytosine and a gallic acid cocrystal of the drug were obtained, and their X-ray crystal structures were also determined in this work.

Low-Wear High-Friction Behavior of Copper Matrix Composites Dispersed With an In Situ Polymer Derived Ceramic

We show that copper-matrix composites that contain 20 vol. % of an in situ processed, polymer-derived, ceramic phase constituted from Si-C-N have unusual friction-and-wear properties. They show negligible wear despite a coefficient of friction (COF) that approaches 0.7. This behavior is ascribed to the lamellar structure of the composite such that the interlamellar regions are infused with nanoscale dispersion of ceramic particles. There is significant hardening of the composite just adjacent to the wear surface by severe plastic deformation.

Adaptive Sleep-Wake Control using Reinforcement Learning in Sensor Networks

The aim in this paper is to allocate the `sleep time' of the individual sensors in an intrusion detection application so that the energy consumption from the sensors is reduced, while keeping the tracking error to a minimum. We propose two novel reinforcement learning (RL) based algorithms that attempt to minimize a certain long-run average cost objective. Both our algorithms incorporate feature-based representations to handle the curse of dimensionality associated with the underlying partially-observable Markov decision process (POMDP). Further, the feature selection scheme used in our algorithms intelligently manages the energy cost and tracking cost factors, which in turn assists the search for the optimal sleeping policy. We also extend these algorithms to a setting where the intruder's mobility model is not known by incorporating a stochastic iterative scheme for estimating the mobility model. The simulation results on a synthetic 2-d network setting are encouraging.

Compressive flow behavior of Al-Si based alloy: Role of heat treatment

The flow characteristics of a near eutectic Al-Si based cast alloy have been examined in compression at strain rates varying from 3 x 10(-4) to 10(2) s(-1) and at three different temperatures, i.e., room temperature (RT), 100 degrees C and 200 degrees C. The dependence of the flow behavior on heat treatment is studied by testing the alloy in non-heat treated (NHT) and heat treated (HT) conditions. The heat treatment has strong influence on strain rate sensitivity (SRS), strength and work hardening behavior of the alloy. It is observed that the strength of the alloy increases with increase in strain rate and it increases more rapidly above the strain rate of 10(-1) s(-1) in HT condition at all the temperatures, and at 100 degrees C and 200 degrees C in NHT condition. The thermally dependent process taking place in the HT matrix is responsible for the observed greater SRS in HT condition. The alloy in HT condition exhibits a larger work hardening rate than in NHT condition during initial stages of straining. However, the hardening rate decreases more sharply at higher strains in HT condition due to precipitate shearing and higher rate of Si particle fracture. Thermal hardening is observed at 200 degrees C in NHT condition due to precipitate formation, which results in increased SRS at higher temperatures. Thermal softening is observed in HT condition at 200 C due to precipitate coarsening, which leads to a decrease in SRS at higher temperatures. Stress simulations by a finite element method support the experimentally observed particle and matrix fracture behavior. A negative SRS and serrated flow are observed in the lower strain rate regime (3 x 10(-4)-10(-2) s(-1)) at RT and 100 degrees C, in both NHT and HT conditions. The observations show that both dynamic strain aging (DSA) and precipitate shearing play a role in serrated flow. (C) 2015 Elsevier B.V. All rights reserved.

Adaptive Mesh Refinement for Fast Convergence of EFIE-Based 3-D Extraction

3-D full-wave method of moments (MoM) based electromagnetic analysis is a popular means toward accurate solution of Maxwell's equations. The time and memory bottlenecks associated with such a solution have been addressed over the last two decades by linear complexity fast solver algorithms. However, the accurate solution of 3-D full-wave MoM on an arbitrary mesh of a package-board structure does not guarantee accuracy, since the discretization may not be fine enough to capture spatial changes in the solution variable. At the same time, uniform over-meshing on the entire structure generates a large number of solution variables and therefore requires an unnecessarily large matrix solution. In this paper, different refinement criteria are studied in an adaptive mesh refinement platform. Consequently, the most suitable conductor mesh refinement criterion for MoM-based electromagnetic package-board extraction is identified and the advantages of this adaptive strategy are demonstrated from both accuracy and speed perspectives. The results are also compared with those of the recently reported integral equation-based h-refinement strategy. Finally, a new methodology to expedite each adaptive refinement pass is proposed.

Electrochemical behavior of Zn-graphene composite coatings

A Zn-graphene composite coating was electrodeposited on mild steel. The graphene was synthesized by electrochemical exfoliation of graphite. Electron microscopy, energy-dispersive X-ray spectroscopy and X-ray diffraction techniques were used to characterize the coatings. Compared to a pure Zn coating, the Zn-graphene coating exhibited reduced grain size, reduced surface defects, hillock structures over the coating surface and an altered texture. The corrosion behavior of the coatings was examined by Tafel polarization and electrochemical impedance spectroscopic methods. A significant improvement in the corrosion resistance in terms of reduction in corrosion current and corrosion rate and increase in polarization resistance was noted in the case of the Zn coating containing graphene.

Orotic acid as a useful supramolecular synthon for the fabrication of an OPV based hydrogel: stoichiometry dependent injectable behavior

A facile hydrogelation of a p-pyridylenevinylene derivative (PV) bearing oxyethylene chains in the presence of orotic acid (OA) occurs via various non-covalent interactions. Depending on the PV: OA molar ratio, the hydrogel shows vesicle to either cluster-type aggregate or fiber transformation. Visual color tuning, stimuli-responsiveness and injectable properties of the hydrogel are also observed.

Adaptive executions of hyperbolic block-structured AMR applications on GPU systems

A block-structured adaptive mesh refinement (AMR) technique has been used to obtain numerical solutions for many scientific applications. Some block-structured AMR approaches have focused on forming patches of non-uniform sizes where the size of a patch can be tuned to the geometry of a region of interest. In this paper, we develop strategies for adaptive execution of block-structured AMR applications on GPUs, for hyperbolic directionally split solvers. While effective hybrid execution strategies exist for applications with uniform patches, our work considers efficient execution of non-uniform patches with different workloads. Our techniques include bin-packing work units to load balance GPU computations, adaptive asynchronism between CPU and GPU executions using a knapsack formulation, and scheduling communications for multi-GPU executions. Our experiments with synthetic and real data, for single-GPU and multi-GPU executions, on Tesla S1070 and Fermi C2070 clusters, show that our strategies result in up to a 3.23 speedup in performance over existing strategies.

Adaptive Pulse Width Control and Sampling for Low Power Pulse Oximetry

Remote sensing of physiological parameters could be a cost effective approach to improving health care, and low-power sensors are essential for remote sensing because these sensors are often energy constrained. This paper presents a power optimized photoplethysmographic sensor interface to sense arterial oxygen saturation, a technique to dynamically trade off SNR for power during sensor operation, and a simple algorithm to choose when to acquire samples in photoplethysmography. A prototype of the proposed pulse oximeter built using commercial-off-the-shelf (COTS) components is tested on 10 adults. The dynamic adaptation techniques described reduce power consumption considerably compared to our reference implementation, and our approach is competitive to state-of-the-art implementations. The techniques presented in this paper may be applied to low-power sensor interface designs where acquiring samples is expensive in terms of power as epitomized by pulse oximetry.

Rheological Behavior of Al-7Si-0.3Mg Alloy at Mushy State

In recent years, semisolid manufacturing has emerged as an attractive option for near net shape forming of components with aluminum alloys. In this class of processes, the key to success lies mainly in the understanding of rheological behavior of the semi-solid slurry in the temperature range between liquidus and solidus. The present study focuses on the non-Newtonian flow behavior of the pseudo plastic slurry of Al-7Si-0.3Mg alloy for a wide shear range using a high-temperature Searle-type rheometer. The rheological behavior of the slurry is studied with respect to relevant process variables and microstructural features such as shear rate, shear duration, temperature history, primary particle size, shape, and their distribution. The experiments performed are isothermal tests, continuous cooling tests, shear jump tests, and shear time tests. The continuous cooling experiments are aimed toward studying the viscosity and shear stress evolution within the slurry matrix with increasing solid fraction at a constant shear rate. Three different cooling rates are considered and their effect on flow behavior of the slurry was studied under iso-shear condition. Descending shear jump experiments are performed to understand the viscous instability of the slurry.