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This paper details an improved modeling technique for a photovoltaic (PV) module; utilizing the optimization ability of a genetic algorithm, with different parameters of the PV module being computed via this approach.
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Using values of, we computed (via simulation) the average power-average utility tradeoff curve for each flow.
For improved speed, we use parallel computing (via MPI).
The fundamental frequencies are computed via the transfer function method.
An optimal LQ-feedback is computed via the solution of a matrix Riccati partial differential equation.
Then, the low-rank factorization matrices are computed via the BRMF to recover the observations.
A state LQ-feedback operator is computed via the solution of a matrix Riccati differential equation in the space variable.
Our newly-formulated measure of mean curvature is computed via the inner product of Laplacian vector and vertex normal.
Approximations of the inherently present temporal convolution are computed via the Convolution Quadrature Method in a nonstandard manner.
Numerically, the fluid flow and membrane deformation are computed, via the particle motion, by a two-step explicit scheme.
The thermal boundary conditions for the simulation domain were computed via the temperature readings which were recorded during the experiments.
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