Numerical analysis of spray-dic modeling for fruit concentration drying process into powder based on computational fluid dynamic

The drying process is most popular preservation methods. It is important in producing the powder and natural dye by concentration fruit drying. Spray-DIC is one of the concentration techniques for drying process using the nozzle flow application in computational fluid dynamic. The mathematical modeling for drying process in this paper includes mass conservation and energy conservation of fruit concentration based on partial differential equation. The discretization of mathematical model will use the finite difference method with the initial and boundary conditions of nozzle flow application. The mathematical modeling computes numerical in sequential algorithm. Jacobi and Gauss-Seidel scheme will use to solve the linear system of mathematical modeling. The execution time, no of iteration, accuracy, root mean square error and maximum error are measured for investigating the numerical analysis. The results show the Gauss Seidel method is the alternative method compared to Jacobi method for solving the Spray-DIC modeling

Comparison between controlled and uncontrolled spray-DIC modeling for dehydration process

The work reported here focuses on the controllability expressions in the mathematical modeling of dehydration process of food concentrates in producing powder using spray-DIC (spray-Détente Instantaneé Controlee or spray-instant controlled pressure drop). This paper presents the second-order partial differential equations for mathematical modeling of moisture and heat transfer in spray-DIC process. This paper proposes the enhancement in the simple model of DIC technique with controllability expression to be used in the spray-DIC. The controllability expression in the drying process models gives better results when compared to the models without the controllability expression. The results were computed and shown by MATLAB 2013 with Windows 8 operating systems. The controllability expression in dehydration process model using the spray-DIC drier manage to succesfully control the dehydration process

Parallel computing of numerical schemes and big data analytic for solving real life applications

This paper proposed the several real life applications for big data analytic using parallel computing software. Some parallel computing software under consideration are Parallel Virtual Machine, MATLAB Distributed Computing Server and Compute Unified Device Architecture to simulate the big data problems. The parallel computing is able to overcome the poor performance at the runtime, speedup and efficiency of programming in sequential computing. The mathematical models for the big data analytic are based on partial differential equations and obtained the large sparse matrices from discretization and development of the linear equation system. Iterative numerical schemes are used to solve the problems. Thus, the process of computational problems are summarized in parallel algorithm. Therefore, the parallel algorithm development is based on domain decomposition of problems and the architecture of difference parallel computing software. The parallel performance evaluations for distributed and shared memory architecture are investigated in terms of speedup, efficiency, effectiveness and temporal performance