Infinite dimensional slow modulations in a well known delayed model for cutting tool vibrations

We apply the method of multiple scales (MMS) to a well-known model of regenerative cutting vibrations in the large delay regime. By ``large'' we mean the delay is much larger than the timescale of typical cutting tool oscillations. The MMS up to second order, recently developed for such systems, is applied here to study tool dynamics in the large delay regime. The second order analysis is found to be much more accurate than the first order analysis. Numerical integration of the MMS slow flow is much faster than for the original equation, yet shows excellent accuracy in that plotted solutions of moderate amplitudes are visually near-indistinguishable. The advantages of the present analysis are that infinite dimensional dynamics is retained in the slow flow, while the more usual center manifold reduction gives a planar phase space; lower-dimensional dynamical features, such as Hopf bifurcations and families of periodic solutions, are also captured by the MMS; the strong sensitivity of the slow modulation dynamics to small changes in parameter values, peculiar to such systems with large delays, is seen clearly; and though certain parameters are treated as small (or, reciprocally, large), the analysis is not restricted to infinitesimal distances from the Hopf bifurcation.

Optimization of two-dimensional NMR by matched accumulation

It is well known that in the time-domain acquisition of NMR data, signal-to-noise (S/N) improves as the square root of the number of transients accumulated. However, the amplitude of the measured signal varies during the time of detection, having a functional form dependent on the coherence detected. Matching the time spent signal averaging to the expected amplitude of the signal observed should also improve the detected signal-to-noise. Following this reasoning, Barna et al. (J Magn. Reson.75, 384, 1987) demonstrated the utility of exponential sampling in one- and two-dimensional NMR, using maximum-entropy methods to analyze the data. It is proposed here that for two-dimensional experiments the exponential sampling be replaced by exponential averaging. The data thus collected can be analyzed by standard fast-Fourier-transform routines. We demonstrate the utility of exponential averaging in 2D NOESY spectra of the protein ubiquitin, in which an enhanced SIN is observed. It is also shown that the method acquires delayed double-quantum-filtered COSY without phase distortion.

The Blackett Memorial Lecture, 1991: Chemical Insights into High-Temperature Superconductors

The high-temperature superconductors are complex oxides, generally containing two-dimensional CuO2 sheets. Various families of the cuprate superconductors are described, paying special attention to aspects related to oxygen stoichiometry, phase stability, synthesis and chemical manipulation of charge carriers. Other aspects discussed are chemical applications of cuprates, possibly as gas sensors and copper-free oxide superconductors. All but the substituted Nd and Pr cuprates are hole-superconductors. Several families of cuprates show a nearly constant n(h) at maximum T(c). Besides this universality, the cuprates exhibit a number of striking common features. Based on Cu(2p) photoemission studies, it is found that the Cu-O charge-transfer energy, DELTA, and the Cu(3d)-O(2p) hybridization strength, t(pd), are key factors in the superconductivity of cuprates. The relative intensity of the satellite in the Cu(2p) core-level spectra, the polarizability of the CuO2 sheets as well as the hole concentration are related to DELTA/t(pd). These chemical bonding factors have to be explicitly taken into account in any model for superconductivity of the cuprates.

Incipient motion design of sand bed channels affected by bed suction

Seepage through sand bed channels in a downward direction (suction) reduces the stability of particles and initiates the sand movement. Incipient motion of sand bed channel with seepage cannot be designed by using the conventional approach. Metamodeling techniques, which employ a non-linear pattern analysis between input and output parameters and solely based on the experimental observations, can be used to model such phenomena. Traditional approach to find non-dimensional parameters has not been used in the present work. Parameters, which can influence the incipient motion with seepage, have been identified and non-dimensionalized in the present work. Non-dimensional stream power concept has been used to describe the process. By using these non-dimensional parameters; present work describes a radial basis function (RBF) metamodel for prediction of incipient motion condition affected by seepage. The coefficient of determination, R-2 of the model is 0.99. Thus, it can be said that model predicts the phenomena very well. With the help of the metamodel, design curves have been presented for designing the sand bed channel when it is affected by seepage. (C) 2010 Elsevier B.V. All rights reserved.

Interaction Energy Based Protein Structure Networks

The three dimensional structure of a protein is formed and maintained by the noncovalent interactions among the amino acid residues of the polypeptide chain These interactions can be represented collectively in the form of a network So far such networks have been investigated by considering the connections based on distances between the amino acid residues Here we present a method of constructing the structure network based on interaction energies among the amino acid residues in the protein We have investigated the properties of such protein energy based networks (PENs) and have shown correlations to protein structural features such as the clusters of residues involved in stability formation of secondary and super secondary structural units Further we demonstrate that the analysis of PENs in terms of parameters such as hubs and shortest paths can provide a variety of biologically important information such as the residues crucial for stabilizing the folded units and the paths of communication between distal residues in the protein Finally the energy regimes for different levels of stabilization in the protein structure have clearly emerged from the PEN analysis

Compensator for constant relative stability in process control systems

Process control systems are designed for a closed-loop peak magnitude of 2dB, which corresponds to a damping coefficient () of 0.5 approximately. With this specified constraint, the designer should choose and/or design the loop components to maintain a constant relative stability. However, the manipulative variable in almost all chemical processes will be the flow rate of a process stream. Since the gains and the time constants of the process will be functions of the manipulative variable, a constant relative stability cannot be maintained. Up to now, this problem has been overcome either by selecting proper control valve flow characteristics or by gain scheduling of controller parameters. Nevertheless, if a wrong control valve selection is made then one has to account for huge loss in controllability or eventually it may lead to an unstable control system. To overcome these problems, a compensator device that can bring back the relative stability of the control system was proposed. This compensator is similar to a dynamic nonlinear controller that has both online and offline information on several factors related to the control system. The design and analysis of the proposed compensator is discussed in this article. Finally, the performance of the compensator is validated by applying it to a two-tank blending process. It has been observed that by using a compensator in the process control system, the relative stability could be brought back to a great extent despite the effects of changes in manipulative flow rate.

Optimal explicit guidance of multistage launch vehicle along three-dimensional trajectory

Details of an efficient optimal closed-loop guidance algorithm for a three-dimensional launch are presented with simulation results. Two types of orbital injections, with either true anomaly or argument of perigee being free at injection, are considered. The resulting steering-angle profile under the assumption of uniform gravity lies in a canted plane which transforms a three-dimensional problem into an equivalent two-dimensional one. Effects of thrust are estimated using a series in a recursive way. Encke's method is used to predict the trajectory during powered flight and then to compute the changes due to actual gravity using two gravity-related vectors. Guidance parameters are evaluated using the linear differential correction method. Optimality of the algorithm is tested against a standard ground-based trajectory optimization package. The performance of the algorithm is tested for accuracy, robustness, and efficiency for a sun-synchronous mission involving guidance for a multistage vehicle that requires large pitch and yaw maneuver. To demonstrate applicability of the algorithm to a range of missions, injection into a geostationary transfer orbit is also considered. The performance of the present algorithm is found to be much better than others.

On the evaluation of stability of rare earth oxides as face coats for investment casting of titanium

Attempts have been made to evaluate the thermal stability of rare earth oxide face coats against liquid titanium. Determination of microhardness profiles and concentration profiles of oxygen and metallic constituents of oxide in investment cast titanium rods has allowed grActation of relative stability of rare earth oxides. The relative stability of evaluated oxides in the order of increasing stability follows the sequence CeO2 — ZrO2 — Gd2O3 — didymium oxide — Sm2O3 —Nd2O3 — Y2O3. The grading does not follow the free energy data of the formation of these oxides. A better correlation with the experimental observations is obtained when the solubility of the metallic species in titanium is also taken into consideration.

Neural network approach for the two-dimensional assignment problem

A neural network approach for solving the two-dimensional assignment problem is proposed. The design of the neural network is discussed and simulation results are presented. The neural network obtains 10-15% lower cost placements on the examples considered, than the adjacent pairwise exchange method.

Analytical solution of the laminar mean flow wave equation in a lined or unlined two-dimensional rectangular duct

The prediction of the sound attenuation in lined ducts with sheared mean flow has been a topic of research for many years. This involves solving the sheared mean flow wave equation, satisfying the relevant boundary condition. As far as the authors' knowledge goes, this has always been done using numerical techniques. Here, an analytical solution is presented for the wave propagation in two-dimensional rectangular lined ducts with laminar mean flow. The effect of laminar mean flow is studied for both the downstream and the upstream wave propagation. The attenuation values predicted for the laminar mean flow case are compared with those for the case of uniform mean flow. Analytical expressions are derived for the transfer matrices.

Integral method for the calculation of incompressible two-dimensional transitional boundary layers

The method proposed here considers the mean flow in the transition zone as a linear combination of the laminar and turbulent boundary layer in proportions determined by the transitional intermittency, the component flows being calculated by approximate integral methods. The intermittency distribution adopted takes into account the possibility of subtransitions within the zone in the presence of strong pressure gradients. A new nondimensional spot formation rate, whose value depends on the pressure gradient, is utilized to estimate the extent of the transition zone. Onset location is determined by a correlation that takes into account freestream turbulence and facility-specific residual disturbances in test data. Extensive comparisons with available experimental results in strong pressure gradients show that the proposed method performs at least as well as differential models, in many cases better, and is always faster.