Proton exchange membrane fuel cell stack design optimization using an improved Jaya algorithm

Fuel cell stack configuration optimization is known to be a problem that, in addition to presenting engineering challenges, is computationally hard. This paper presents an improved computational heuristic for solving the problem. The problem addressed in this paper is one of constrained optimization, where the goal is to seek optimal (or near-optimal) values of (i) the number of proton exchange membrane fuel cells (PEMFCs) to be connected in series to form a group, (ii) the number of such groups to be connected in parallel, and (iii) the cell area, such that the PEMFC assembly delivers the rated voltage at the rated power while the cost of building the assembly is as low as possible. Simulation results show that the proposed method outperforms four of the best-known methods in the literature. The improvement in performance afforded by the proposed algorithm is validated with statistical tests of significance

Semi-steady-state Jaya Algorithm

The Jaya algorithm is arguably one of the fastest-emerging metaheuristics amongst the newest members of the evolutionary computation family. The present paper proposes a new, improved Jaya algorithm by modifying the update strategies of the best and the worst members in the population. Simulation results on a twelve-function benchmark test-suite as well as a real-world problem of practical importance show that the proposed strategy produces results that are better and faster in the majority of cases. Statistical tests of significance are used to validate the performance improvement

Reversible and irreversible potentials and an inaccuracy in popularmodels in the fuel cell literature

Modeling is an integral part of fuel cell design and development. This paper identifies a long-standing inaccuracy in the fuel cell modeling literature. Specifically, it discusses an inexact insertion, in popular models, of cell/stack current into Nernst\u27s equation in the derivation of output (load) voltage. The origin of the inaccuracy is traced to the nature of reversible and irreversible potentials (equilibrium and non-equilibrium states) in the cell. The significance of the inaccuracy is explained in the context of the electrochemistry and thermodynamics of the fuel cell

Semi-steady-state jaya algorithm for optimization

The Jaya algorithm is arguably one of the fastest-emerging metaheuristics amongst the newest members of the evolutionary computation family. The present paper proposes a new, improved Jaya algorithm by modifying the update strategies of the best and the worst members in the population. Simulation results on a twelve-function benchmark test-suite and a real-world problem show that the proposed strategy produces results that are better and faster in the majority of cases. Statistical tests of significance are used to validate the performance improvement

A new model for constant fuel utilization and constant fuel flow in fuel cells

This paper presents a new model of fuel cells for two different modes of operation: constant fuel utilization control (constant stoichiometry condition) and constant fuel flow control (constant flow rate condition). The model solves the long-standing problem of mixing reversible and irreversible potentials (equilibrium and non-equilibrium states) in the Nernst voltage expression. Specifically, a Nernstian gain term is introduced for the constant fuel utilization condition, and it is shown that the Nernstian gain is an irreversibility in the computation of the output voltage of the fuel cell. A Nernstian loss term accounts for an irreversibility for the constant fuel flow operation. Simulation results are presented. The model has been validated against experimental data from the literature

Two-Layered Pulsatile Blood Flow in a Stenosed Artery with Body Acceleration and Slip at Wall

Pulsatile flow of blood through an artery in presence of a mild stenosis has been investigated in this paper assuming the body fluid blood as a two-fluid model with the suspension of all the erythrocytes in the core region as Bingham Plastic and the peripheral region of plasma as a Newtonian fluid. This model has been used to study the influence of body acceleration, non- Newtonian nature of blood and a velocity slip at wall, in blood flow through stenosed arteries. By employing a perturbation analysis, analytic expressions for the velocity profile, Plug-core radius, flow rate, wall shear stress and effective viscosity, are derived. The variations of flow variables with different parameters are shown diagrammatically and discussed. It is noticed that velocity and flow rate increase but effective viscosity decreases, due to a wall slip. Flow rates and speed are enhanced further due to the influence of body acceleration

Pulsatile Flow of Blood in a Constricted Artery with Body Acceleration

Pulsatile flow of blood through a uniform artery in the presence of a mild stenosis has been investigated in this paper. Blood has been represented by a Newtonian fluid. This model has been used to study the influence of body acceleration and a velocity slip at wall, in blood flow through stenosed arteries. By employing a perturbation analysis, analytic expressions for the velocity profile, flow rate, wall shear stress and effective viscosity, are derived. The variations of flow variables with different parameters are shown diagrammatically and discussed. It is noticed that velocity and flow rate increase but effective viscosity decreases, due to a wall slip. Flow rate and speed enhance further due to the influence of body acceleration. Biological implications of this modeling are briefly discussed

Gene pool recombination, genetic algorithm, and the onemax function

In this paper we present an analysis of gene pool recombination in genetic algorithms in the context of the onemax function. We have developed a Markov chain framework for computing the probability of convergence, and have shown how the analysis can be used to estimate the critical population size. The Markov model is used to investigate drift in the multiple-loci case. Additionally, we have estimated the minimum population size needed for optimality, and recurrence relations describing the growth of the advantageous allele in the infinite-population case have been derived. Simulation results are presented