ANN

Economy of the product yarn are the basic aspects; spinner should always need to be conscious about it. Formulation of the mix is the outcome of these constraints. But the selection of right components and their proportion in mix to reach the above mentioned target demands lot of brainstorming exercise, statistics and the human skills involved in this course. Drastic changes took place in the conventional practices used for the formulation of the mix to that of present practices going on. The limitations of conventional methods have been identified. The present method with the adoption of ANN, HVI and EFS has overcome these limitations to a greater extent. This paper explores in brief limitations of conventional approach; present method used along with the expected future trends.

An artificial neural network (ANN) model was developed for the analysis and simulation of the correlation between the friction stir welding (FSW) parameters of aluminum (Al) plates and mechanical properties. The input parameters of the model consist of weld speed (Ws) and tool rotation speed (Rs). The outputs of the ANN model include property parameters namely: tensile strength, yield strength and elongation. Good performance of the ANN model was achieved. The model can be used to calculate mechanical properties of welded Al plates as functions of weld speed and Rs. The combined influence of weld speed and Rs on the mechanical properties of welded Al plates was simulated. Simulated annealing technique was used to prevent the network from getting stuck in local minima. A comparison was made between measured and calculated data. The calculated results were in good agreement with measured data.

The Direct Numerical Optimization (DNO) approach for airfoil shape design requires the integration of modules: a) A geometrical shape function; b) Computational flow solver and; c) Search model for shape optimization. These modules operate iteratively until convergence based on defined objectives and constraints. The DNO architecture is to be validated to ensure efficient optimization simulations and is the focus of this paper. The PARSEC airfoil shape function is first validated by observing the effect of design coefficients on airfoil geometry and aerodynamics. The design variables provide independent one-to-one control over airfoil geometry, for imposing shape constraints. The aerodynamic performance of PARSEC airfoils through variable perturbations, conform to established aerodynamic principles. It confirms the design flexibility of the shape function in providing direct control over airfoil geometry. The Particle Swarm Optimization (PSO) algorithm is introduced as the search agent. A PSO simulation requires userinputs to define the search pattern. A methodology is presented to validate these parameters on pre-defined benchmark mathematical functions. Self Organizing Maps (SOM) are applied to illustrate trade-offs between PSO search variables. An Adaptive Inertia Weight (APSO) scheme that dynamically alters the search path of the swarm by monitoring the position of the particles, provides an acceptable convergence. Validation tests indicated the maximum velocity of the particles is less than 1% of computational domain size for convergence. The DNO approach is computationally inefficient, thus a surrogate model to address this issue is presented. An Artificial Neural Network (ANN) model with a training dataset of 3000 airfoils is applied to develop a model that applies the PARSEC airfoil geometry variables as inputs and the equating aerodynamic coefficient as output. System validation with 1000 randomly generated airfoils indicated 70% of the simulated solutions were within 10% of actual solver run. Future research will involve reducing the percentage error of the surrogate model against the theoretical solution.

In this paper the concept of Artificial Neural Network (ANN) was implemented in constructing an Adaptive Single Phase AR (ASPAR) relay. The heart of this relay is an ANN trained to discriminate healthy system, permanent fault, transient fault and extinguishing of the secondary arc. Thus issuing right decisions. The proposed ANN method is trained only to recognize single phase to ground fault types (both transient and permanent) simulated on TL Model between Baijie Thermal Power Station and Mosul Super Grid Sub-Station 400Kv from Iraqi North Regional Grids (INRG).