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Michele Pinelli

Researcher at University of Ferrara

Publications -  208
Citations -  2508

Michele Pinelli is an academic researcher from University of Ferrara. The author has contributed to research in topics: Gas compressor & Axial compressor. The author has an hindex of 24, co-authored 188 publications receiving 2013 citations.

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NARX models for simulation of the start-up operation of a single-shaft gas turbine

TL;DR: In this article, nonlinear autoregressive exogenous (NARX) models of a heavy-duty single-shaft gas turbine (GT) are developed and validated using three time series data sets of two different maneuvers taken experimentally during the start-up procedure.
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Thermophotovoltaic energy conversion: Analytical aspects, prototypes and experiences

TL;DR: In this article, the characteristics of a TPV generator are analyzed with a particular attention to the physical relationships which govern the behavior of its main components, including combustor, emitter, optical filter and photovoltaic cells.
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Overview and Status of Thermophotovoltaic Systems

TL;DR: In this paper, the characteristics of a thermophotovoltaic generator are analyzed with a particular attention to the physical relationships which govern the behavior of its main components, including combustor, the emitter, the optical filter and the photovoltaic cells.
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Development of an equilibrium model for the simulation of thermochemical gasification and application to agricultural residues

TL;DR: In this article, a model for the simulation of the biomass thermochemical gasification process is presented based on a chemical equilibrium hypothesis, i.e. it is assumed that the chemical transformations inside the gasifier can be described by a series of equilibrium reactions independent of each other and, therefore, a precise relationship between products and reactants can be determined.
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Methodology for estimating biomass energy potential and its application to Colombia

TL;DR: In this paper, a bottom-up resource-focused approach with statistical analysis that uses a Monte Carlo algorithm to stochastically estimate the theoretical and the technical biomass energy potential is presented.