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A simple mathematical model of stack ventilation flows in multi-compartment buildings is developed with a view to providing an intuitive understanding of the physical processes governing the movement of air and heat through naturally ventilated buildings.
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In fact, since the portability property of Java rests upon its interpretative execution model of stack-based bytecode, optimizations by static and JIT compilers may or may not be effective for achieving high performance in various scientific code.
One model capable of describing the intensity is a model of stacked cylinders.
Modeling results under time-invariant forcing are consistent with generalized conceptual models of stacking patterns.
In this study, an experimentally validated analytical-numerical model of the stack is used to investigate the effect of manifold and cooling flow field channel dimensions on stack temperature uniformity.
And building up the control-oriented thermal model of PEMFC stack is necessary.
A simple thermal model of the stack is developed and validated, and accounts for heat losses by radiation and convection.
Finally using the neural networks model of MCFC stack, the simulation results of the control algorithm are presented.
The simulation tests reveal that it is feasible to establish the model of MCFC stack using RBF neural networks identification.
This paper presents an application of radial basis functions (RBF) neural networks identification to develop a nonlinear temperature model of MCFC stack.
Pre-multiplication of a single-frequency reference signal by the nonlinear model of the stack is investigated in order to effectively compensate the actuator nonlinearity.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com