S. Venugopal ¹, V. Ramanujachari ¹, K. K. Rajesh ¹

Affiliations
1 Defence Research and Development Laboratory, Hyderabad – 500 058

Abstract

A series of combustion experiments have been conducted for investigating the regression rates of mixed hybrid propellants using Red Fuming Nitrous Acid (RFNA) as liquid oxidizer and Hydroxyl-Terminated Polybutadiene (HTPB) with the addition of Ammonium Perchlorate (AP) upto 20 percent as the solid fuel. HTPB is the state-of-theart binder used in solid propellant rocket motors and is considered to be a potential candidate fuel for hybrid rocket applications due to its higher regression rate characteristics, greater fuel value, higher carbon/hydrogen ratio and solid loading capability. One of the effective ways of increasing the regression rates in hybrids is the addition of solid oxidizer such as AP in the fuel in small percentages. This type of configuration is called "mixed hybrid" and regression rate enhancement upto 100% is obtained. A series of static tests are conducted to establish the ignition and combustion in the hybrid mode. It is seen that the hybrid propellant has burnt smoothly. The ignition pressure could also be controlled and kept to a reasonable value. Stable combustion within a variation of chamber pressure ±2% and combustion efficiency above 0.95 was achieved. The characteristics of the combustion products are calculated using the NASA CEA code. Regression rate correlations for the different combinations are obtained using the experimental data and ballistic code developed for predicting the performance of hybrid system.

Keywords

Hybrid, Regression Rate, Mass Flux, Specific Impulse, Solid Fuel, Liquid Oxidizer

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K. S. Suresh ¹, V. Vaithiyanathan ¹, S. Venugopal ²

Affiliations
1 School of Computing, SASTRA University, Thanjavur - 613401 Tamil Nadu, IN
2 Indira Gandhi Centre for Atomic Research, Kalpakkam - 603102, Tamil Nadu, IN

Abstract

Travelling Salesman Problem (TSP) is a very old problem which has been solved in so many methodologies. The solution for the Robot Path Planning (RPP) can be derived using the methodologies used for the TSP. In this paper, Genetic Algorithm (GA) principle is employed to solve the TSP and is mapped to solve the RPP with the same principle. The both problems are defined to observe similarity between these problems and enumerated the conversion phases. In these two cases how the solutions are to be derived to implement the GA technique to accomplish the optimal path in both cases and tested for different number of cities, population space and generations. The minimum cost and mean cost of the solution space proves its giving the optimal result

Keywords

ASCII, Encryption, Hackers, Random Number, Symmetric Key

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K. S. Suresh ¹, V. Vaithiyanathan ¹, S. Venugopal ²

Affiliations
1 School of Computing, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
2 Indira Gandhi Centre for Atomic Research, Kalpakkam - 603102, Tamil Nadu, IN

Abstract

Robot Path Planning (RPP) is solved using Genetic Algorithm (GA) principle for collision-free navigation for the three dimensional static space to find the optimal path. In this paper, Layered Approach is employed where the whole three dimensional space is considered as layers of two dimensional spaces to accomplish the RPP to reach the target by avoiding obstacles and find the shortest path. The quality of the path is ensured by nearest neighbourhood approach. Implementation of the principle of GA to solve RPP is effective where the environment contains huge number of solution paths compared to the classical methods to obtain the shortest path from source to target. This approach is tested for different number of layers and the results are tabled. The path generated and optimal path obtained by the implementation of this approach has been compared with the cost of the optimal path obtained manually.

Keywords

Collision-Free, Genetic Algorithm, Robot Path Planning, Three Dimensional Spaces

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