Blade manipulators in turbulent channel flow

We report here the results of a series of careful experiments in turbulent channel flow, using various configurations of blade manipulators suggested as optimal in earlier boundary layer studies. The mass flow in the channel could be held constant to better than 0.1%, and the uncertainties in pressure loss measurements were less than 0.1 mm of water; it was therefore possible to make accurate estimates of the global effects of blade manipulation of a kind that are difficult in boundary layer flows. The flow was fully developed at the station where the blades were mounted, and always relaxed to the same state sufficiently far downstream. It is found that, for a given mass flow, the pressure drop to any station downstream is always higher in the manipulated than in the unmanipulated flow, demonstrating that none of the blade manipulators tried reduces net duct losses. However the net increase in duct losses is less than the drag of the blade even in laminar flow, showing that there is a net reduction in the total skin friction drag experienced by the duct, but this relief is only about 20% of the manipulator drag at most.

Effect of uncertainty on helicopter performance predictions

The effect of uncertainties on performance predictions of a helicopter is studied in this article. The aeroelastic parameters such as the air density, blade profile drag coefficient, main rotor angular velocity, main rotor radius, and blade chord are considered as uncertain variables. The propagation of these uncertainties in the performance parameters such as thrust coefficient, figure of merit, induced velocity, and power required are studied using Monte Carlo simulation and the first-order reliability method. The Rankine-Froude momentum theory is used for performance prediction in hover, axial climb, and forward flight. The propagation of uncertainty causes large deviations from the baseline deterministic predictions, which undoubtedly affect both the achievable performance and the safety of the helicopter. The numerical results in this article provide useful bounds on helicopter power requirements.

Integrated modelling approaches to analysis of climate change impacts on forests and forest management

This paper reviews integrated economic and ecological models that address impacts and adaptation to climate change in the forest sector. Early economic model studies considered forests as one out of many possible impacts of climate change, while ecological model studies tended to limit the economic impacts to fixed price-assumptions. More recent studies include broader representations of both systems, but there are still few studies which can be regarded fully integrated. Full integration of ecological and economic models is needed to address forest management under climate change appropriately. The conclusion so far is that there are vast uncertainties about how climate change affects forests. This is partly due to the limited knowledge about the global implications of the social and economical adaptation to the effects of climate change on forests.

A new Fuzzy-LOGIC based Model for Chlorophyll-a in Pulicat Lagoon, India

Coastal lagoons are complex ecosystems exhibiting a high degree of non-linearity in the distribution and exchange of nutrients dissolved in the water column due to their spatio-temporal characteristics. This factor has a direct influence on the concentrations of chlorophyll-a, an indicator of the primary productivity in the water bodies as lakes and lagoons. Moreover the seasonal variability in the characteristics of large-scale basins further contributes to the uncertainties in the data on the physico-chemical and biological characteristics of the lagoons. Considering the above, modelling the distributions of the nutrients with respect to the chlorophyll-concentrations, hence requires an effective approach which will appropriately account for the non-linearity of the ecosystem as well as the uncertainties in the available data. In the present investigation, fuzzy logic was used to develop a new model of the primary production for Pulicat lagoon, Southeast coast of India. Multiple regression analysis revealed that the concentrations of chlorophyll-a in the lagoon was highly influenced by the dissolved concentrations of nitrate, nitrites and phosphorous to different extents over different seasons and years. A high degree of agreement was obtained between the actual field values and those predicted by the new fuzzy model (d = 0.881 to 0.788) for the years 2005 and 2006, illustrating the efficiency of the model in predicting the values of chlorophyll-a in the lagoon.

Cooperative Spectrum Sensing Algorithms For OFDM Systems with Frequency Selective Channels

This paper considers the problem of spectrum sensing in cognitive radio networks when the primary user employs Orthogonal Frequency Division Multiplexing (OFDM). We specifically consider the scenario when the channel between the primary and a secondary user is frequency selective. We develop cooperative sequential detection algorithms based on energy detectors. We modify the detectors to mitigate the effects of some common model uncertainties such as timing and frequency offset, IQ-imbalance and uncertainty in noise and transmit power. The performance of the proposed algorithms are studied via simulations. We show that the performance of the energy detector is not affected by the frequency selective channel. We also provide a theoretical analysis for some of our algorithms.

Robust/Optimal Temperature Profile Control of a High-Speed Aerospace Vehicle Using Neural Networks

An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A I-D distributed parameter model of a fin is developed from basic thermal physics principles. "Snapshot" solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the "proper orthogonal decomposition" (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.

Effective drug dosage design for treatment of infectious diseases using dynamic inversion

A generic nonlinear mathematical model describing the human immunological dynamics is used to design an effective automatic drug administration scheme. Even though the model describes the effects of various drugs on the dynamic system, this work is confined to the drugs that kill the invading pathogen and heal the affected organ. From a system theoretic point of view, the drug inputs can be interpreted as control inputs, which can be designed based on control theoretic concepts. The controller is designed based on the principle of dynamic inversion and is found to be effective in curing the �nominal model patient� by killing the invading microbes and healing the damaged organ. A major advantage of this technique is that it leads to a closed-form state feedback form of control. It is also proved from a rigorous mathematical analysis that the internal dynamics of the system remains stable when the proposed controller is applied. A robustness study is also carried out for testing the effectiveness of the drug administration scheme for parameter uncertainties. It is observed from simulation studies that the technique has adequate robustness for many �realistic model patients� having off-nominal parameter values as well.

Optimal blood glucose regulation of diabetic patients using single network adaptive critics

Diabetes is a long-term disease during which the body's production and use of insulin are impaired, causing glucose concentration level to increase in the bloodstream. Regulating blood glucose levels as close to normal as possible leads to a substantial decrease in long-term complications of diabetes. In this paper, an intelligent online feedback-treatment strategy is presented for the control of blood glucose levels in diabetic patients using single network adaptive critic (SNAC) neural networks (which is based on nonlinear optimal control theory). A recently developed mathematical model of the nonlinear dynamics of glucose and insulin interaction in the blood system has been revised and considered for synthesizing the neural network for feedback control. The idea is to replicate the function of pancreatic insulin, i.e. to have a fairly continuous measurement of blood glucose and a situation-dependent insulin injection to the body using an external device. Detailed studies are carried out to analyze the effectiveness of this adaptive critic-based feedback medication strategy. A comparison study with linear quadratic regulator (LQR) theory shows that the proposed nonlinear approach offers some important advantages such as quicker response, avoidance of hypoglycemia problems, etc. Robustness of the proposed approach is also demonstrated from a large number of simulations considering random initial conditions and parametric uncertainties. Copyright (C) 2009 John Wiley & Sons, Ltd.

Climate change and agricultrue-An Indian perspective

In this paper, we examine the major predictions made so far regarding the nature of climate change and its impacts on our region in the light of the known errors of the set of models and the observations over this century. The major predictions of the climate models about the impact of increased concentration of greenhouse gases ave at variance with the observations over the Indian region during the last century characterized by such increases and global warming. It is important to note that as far as the Indian region is concerned, the impact of year-to-year variation of the monsoon will continue to be dominant over longer period changes even in the presence of global warming. Recent studies have also brought out the uncertainties in the yields simulated by crop models. It is suggested that a deeper understanding of the links between climate and agricultural productivity is essential for generating reliable predictions of impact of climate change. Such an insight is also required for identifying cropping patterns and management practices which are tailored for sustained maximum yield in the face of the vagaries of the monsoon.

Towards building an intelligent decision support system for project management

Management of large projects, especially the ones in which a major component of R&D is involved and those requiring knowledge from diverse specialised and sophisticated fields, may be classified as semi-structured problems. In these problems, there is some knowledge about the nature of the work involved, but there are also uncertainties associated with emerging technologies. In order to draw up a plan and schedule of activities of such a large and complex project, the project manager is faced with a host of complex decisions that he has to take, such as, when to start an activity, for how long the activity is likely to continue, etc. An Intelligent Decision Support System (IDSS) which aids the manager in decision making and drawing up a feasible schedule of activities while taking into consideration the constraints of resources and time, will have a considerable impact on the efficient management of the project. This report discusses the design of an IDSS that helps in project planning phase through the scheduling phase. The IDSS uses a new project scheduling tool, the Project Influence Graph (PIG).

Tie lines and activities in the system NiO-MgO-SiO2 at 1373 K

The isothermal section of the phase diagram for the system NiO-MgO-SiO2 at 1373 K is established, The tie lines between (NiXMg1-X)O solid solution with rock salt structure and orthosilicate solid solution (NiYMg1-Y)Si0.5O2 and between orthosilicate and metasilicate (NiZMg1-Z)SiO3 crystalline solutions are determined using electron probe microanalysis (EPMA) and lattice parameter measurement on equilibrated samples, Although the monoxides and orthosilicates of Ni and Mg form a continuous range of solid solutions, the metasilicate phase exists only for 0 < Z < 0.096, The activity of NiO in the rock salt solid solution is determined as a function of composition and temperature in the range of 1023 to 1377 K using a solid state galvanic cell, The Gibbs energy of mixing of the monoxide solid solution can be expressed by a pseudo-subregular solution model: Delta G(ex) = X(1 - X)[(-2430 + 0.925T)X + (-5390 + 1.758T)(1 - X)] J/mol, The thermodynamic data for the rock salt phase are combined with information on interphase partitioning of Ni and Mg to generate the mixing properties for the orthosilicate and the metasilicate solid solutions, The regular solution model describes the orthosilicate and the metasilicate solid solutions at 1373 K within experimental uncertainties, The regular solution parameter Delta G(ex)/Y(1 - Y) is -820 (+/-70) J/mol for the orthosilicate solid solution, The corresponding value for the metasilicate solid solution is -220 (+/-150) J/mol, The derived activities for the orthosilicate solid solution are discussed in relation to the intracrystalline ion exchange equilibrium between M1 and M2 sites. The tie line information, in conjunction with the activity data for orthosilicate and metasilicate solid solutions, is used to calculate the Gibbs energy changes for the intercrystalline ion exchange reactions, Combining this with the known data for NiSi0.5O2, Gibbs energies of formation of MgSi0.5O2, MgSiO3, and metastable NiSiO3 are calculated, The Gibbs energy of formation of NiSiO3, from its component oxides, is equal to 7.67 (+/-0.6) kJ/mol at 1373 K.

Translational velocity oscillations of piston generated vortex rings

Experimental results are presented that show that the translational velocities of piston generated vortex rings often undergo oscillations, similar to those recently discovered for drop generated rings. An attempt has been made to minimize uncertainties by utilizing both dye and hydrogen bubbles for visualization and carefully repeating measurements on the same ring and on different realizations under the same nominal piston conditions. The results unambiguously show that under most conditions, both for laminar and turbulent rings and for rings generated from pipes and orifices, the oscillations are present. The present results, together with the earlier results on drop generated rings, give support to the view that translational velocity oscillations are probably an inherent feature of translating vortex ring fields. (C) 1995 American Institute of Physics.

Predictive uncertainty of chaotic daily streamflow using ensemble wavelet networks approach

Perfect or even mediocre weather predictions over a long period are almost impossible because of the ultimate growth of a small initial error into a significant one. Even though the sensitivity of initial conditions limits the predictability in chaotic systems, an ensemble of prediction from different possible initial conditions and also a prediction algorithm capable of resolving the fine structure of the chaotic attractor can reduce the prediction uncertainty to some extent. All of the traditional chaotic prediction methods in hydrology are based on single optimum initial condition local models which can model the sudden divergence of the trajectories with different local functions. Conceptually, global models are ineffective in modeling the highly unstable structure of the chaotic attractor. This paper focuses on an ensemble prediction approach by reconstructing the phase space using different combinations of chaotic parameters, i.e., embedding dimension and delay time to quantify the uncertainty in initial conditions. The ensemble approach is implemented through a local learning wavelet network model with a global feed-forward neural network structure for the phase space prediction of chaotic streamflow series. Quantification of uncertainties in future predictions are done by creating an ensemble of predictions with wavelet network using a range of plausible embedding dimensions and delay times. The ensemble approach is proved to be 50% more efficient than the single prediction for both local approximation and wavelet network approaches. The wavelet network approach has proved to be 30%-50% more superior to the local approximation approach. Compared to the traditional local approximation approach with single initial condition, the total predictive uncertainty in the streamflow is reduced when modeled with ensemble wavelet networks for different lead times. Localization property of wavelets, utilizing different dilation and translation parameters, helps in capturing most of the statistical properties of the observed data. The need for taking into account all plausible initial conditions and also bringing together the characteristics of both local and global approaches to model the unstable yet ordered chaotic attractor of a hydrologic series is clearly demonstrated.

Multivariate nonlinear ensemble prediction of daily chaotic rainfall with climate inputs

The basic characteristic of a chaotic system is its sensitivity to the infinitesimal changes in its initial conditions. A limit to predictability in chaotic system arises mainly due to this sensitivity and also due to the ineffectiveness of the model to reveal the underlying dynamics of the system. In the present study, an attempt is made to quantify these uncertainties involved and thereby improve the predictability by adopting a multivariate nonlinear ensemble prediction. Daily rainfall data of Malaprabha basin, India for the period 1955-2000 is used for the study. It is found to exhibit a low dimensional chaotic nature with the dimension varying from 5 to 7. A multivariate phase space is generated, considering a climate data set of 16 variables. The chaotic nature of each of these variables is confirmed using false nearest neighbor method. The redundancy, if any, of this atmospheric data set is further removed by employing principal component analysis (PCA) method and thereby reducing it to eight principal components (PCs). This multivariate series (rainfall along with eight PCs) is found to exhibit a low dimensional chaotic nature with dimension 10. Nonlinear prediction employing local approximation method is done using univariate series (rainfall alone) and multivariate series for different combinations of embedding dimensions and delay times. The uncertainty in initial conditions is thus addressed by reconstructing the phase space using different combinations of parameters. The ensembles generated from multivariate predictions are found to be better than those from univariate predictions. The uncertainty in predictions is decreased or in other words predictability is increased by adopting multivariate nonlinear ensemble prediction. The restriction on predictability of a chaotic series can thus be altered by quantifying the uncertainty in the initial conditions and also by including other possible variables, which may influence the system. (C) 2011 Elsevier B.V. All rights reserved.

Roy equation analysis of ππ scattering

We analyse the Roy equations for the lowest partial waves of elastic ππ scattering. In the first part of the paper, we review the mathematical properties of these equations as well as their phenomenological applications. In particular, the experimental situation concerning the contributions from intermediate energies and the evaluation of the driving terms are discussed in detail. We then demonstrate that the two S-wave scattering lengths a00 and a02 are the essential parameters in the low energy region: Once these are known, the available experimental information determines the behaviour near threshold to within remarkably small uncertainties. An explicit numerical representation for the energy dependence of the S- and P-waves is given and it is shown that the threshold parameters of the D- and F-waves are also fixed very sharply in terms of a00 and a20. In agreement with earlier work, which is reviewed in some detail, we find that the Roy equations admit physically acceptable solutions only within a band of the (a00,a02) plane. We show that the data on the reactions e+e−→ππ and τ→ππν reduce the width of this band quite significantly. Furthermore, we discuss the relevance of the decay K→ππeν in restricting the allowed range of a00, preparing the grounds for an analysis of the forthcoming precision data on this decay and on pionic atoms. We expect these to reduce the uncertainties in the two basic low energy parameters very substantially, so that a meaningful test of the chiral perturbation theory predictions will become possible.