The present status and the projected programme of Zirconium development in India

THE nuclear power industry continues to be the major consumer of zirconium metal production in the world today. On the basis of neutron economy, corrosion resistance and mechanical strength, zirconium alloys have been the ideal choice for the fuel-cladding and other core components in watercooled nuclear power systems. In the United States alone, the current annual requirement of zircaloy tubing for nuclear fuel cladding has been placed at 250 tons, which will grow to 600 tons by 1970 and 900 tons by 1973. In India, for the 1200 MW(e) nuclear power programme envisaged for the IV Plan period, zircaloy tube requir-ements have been estimated at 50 tons per year and will increase to 75 tons and more during the V Plan period

Bubbling and bistability in two parameter discrete systems

We present a graphical analysis of the mechanisms underlying the occurrences of bubbling sequences and bistability regions in the bifurcation scenario of a special class of one dimensional two parameter maps. The main result of the analysis is that whether it is bubbling or bistability is decided by the sign of the third derivative at the inflection point of the map function.Comment: LaTeX v2.09, 14 pages with 4 PNG figure

Fault toleranat neuro/neuro-fuzzy controllers for aircraft auto landing with actuator failures and severe winds

In this talk, we will cover some recent work carried out in our group for developing intelligent flight controllers which can handle failures and also flying under severe winds. Using neural networks and also fuzzy neural networks the controllers are able to handle actuator failures and able to perform similar to those under normal conditions. Specifically, we will look at 1. Neural-aided controller 2. Fuzzy-neural aided controller and 3. Adaptive back stepping neural controller architectures for handling failures under severe winds for an aircraft auto-landing problem. The results indicate that good fault tolerant capabilities can be provided for existing controllers without adding more complexities and computational overhead

Adaptive backstepping neural controller for aircraft autolanding

This report presents an adaptive back-stepping neural controller for reconfigurable flight control of aircraft in the presence of changes in the aerodynamic characteristics. Radial Basis Function Neural networks are introduced in an adaptive back stepping architecture13; with full state measurement for aircraft trajectory following. For the RBF neural network a learning scheme in which the network starts with no hidden neurons and adds new hidden neurons based on the trajectory error is proposed. Using Lyapunov theory stable tuning rules are derived for parameter update of the RBF neural networks and proof of stability in the ultimate bounded sense is given for the resulting controller. The longitudinal model of an open loop unstable high performance aircraft in the terminal landing phase subjected to single elevator hard over failures is used to demonstrate the capability of the controller. The resulting controller is able to successfully stabilize and land the aircraft in the presence of severe winds and control surface failures

Radial basis function neural networks with sequential learning: MRAN and its applications

This book presents in detail the newly developed sequential learning algorithm for radial basis function neural networks, which realizes a minimal network. This algorithm, created by the authors, is referred to as Minimal Resource Allocation Networks (MRAN). The book describes the application of MRAN in different areas, including pattern recognition, time series prediction, system identification, control, communication and signal processing. Benchmark problems from these areas have been studied, and MRAN is compared with other algorithms. In order to make the book self-contained, a review of