Search

Your search keyword '"Mazzei, Lorenzo"' showing total 132 results
Start Over Author "Mazzei, Lorenzo"
132 results on '"Mazzei, Lorenzo"'

22. Analysis of a Stator-Rotor-Stator Spinning Disk Reactor in Single-Phase and Two-Phase Boiling Conditions Using a Thermo-Fluid Flow Network and CFD

Author

Mazzei, Lorenzo, Marin, Francesco Maria, Bianchini, Cosimo, Da Soghe, Riccardo, Bertani, Cristina, Pastrone, Dario, Angelucci, Maddalena, Caggiano, Giuseppe, de Beer, Michiel, Mazzei, Lorenzo, Marin, Francesco Maria, Bianchini, Cosimo, Da Soghe, Riccardo, Bertani, Cristina, Pastrone, Dario, Angelucci, Maddalena, Caggiano, Giuseppe, and de Beer, Michiel

Abstract

Cryogenic liquid propellants are used in liquid rocket engines to obtain high specific impulse. The flow rates are controlled by turbopumps that deliver liquid propellant to the engine at high pressure levels. Due to the very low saturation temperature of the cryogenic propellant, in the first phases of the transient operation, in which the engine is at ambient temperature, its surfaces are subject to boiling conditions. The effect of boiling on the heat transfer between the solid and the fluid needs to be well characterized in order to correctly predict the cryopump metal temperature temporal evolution and the necessary amount of propellant. With the aim of benchmarking numerical tools against experimental data, a representative test case was chosen. This consists of a stator-rotor-stator spinning disc reactor studied under single-phase and two-phase heat transfer conditions. The numerical approaches used are represented by a 1D network solver, where the pressure drop and heat transfer are calculated by correlations, and Computational Fluid Dynamics (CFD) simulations, carried out with ANSYS Fluent. Both the numerical tools returned a reasonable agreement in single-phase conditions, also thanks to the use of adequate correlations in the flow network solver and typical conditions for the CFD simulations. Two-phase conditions on the contrary are more challenging, with underpredictions up to 20% and 80%, respectively. The issues are ascribable to the use of correlations that are inadequate to capture the two-phase phenomena occurring in the srs reactor and numerical limitations in the actual implementation of the boiling model in the CFD solver.

Published
2022

49. Numerical Investigation of Surface Roughness Effects on Heat Transfer in a Turbine Cascade.

Author

Subramanian, Arunprasath, Gamannossi, Andrea, Mazzei, Lorenzo, and Andreini, Antonio

Subjects
HEAT transfer, SURFACE roughness, GAS turbines, TURBINES, REYNOLDS number, TURBINE blades
Abstract

Turbine entry temperature has seen a steady increase over the years as it is directly linked to the overall efficiency of a gas turbine engine, thus determining fuel consumption and ultimately CO2 emissions. An accurate estimation of the heat loads is of paramount importance to design the cooling system and determine the life-span of the component, therefore it is mandatory to assess the capability of CFD to predict the involved phenomena. Surface roughness plays a key role in determining the heat transfer and becomes relevant especially in case of in-service degradation and additive-manufactured components. It is seen that for a turbine blade in service for about a thousand hours, the surface roughness has similar effects as turbulence on the heat transfer. This paper is focused on the study of the influence of roughness, turbulence and Reynolds number on the aerodynamics and heat transfer by using commercial code ANSYS Fluent. Transition was accounted exploiting the Transition-SST model, whereas roughness was modeled with an equivalent sand grain approach. Numerical results have been compared against experimental data from the literature. [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
View/download PDF

50. Uncertainty Quantification of an Aeronautical Combustor using a 1-D Approach.

Author

Gamannossi, Andrea, Amerini, Alberto, Poggiali, Matteo, Elmi, Carlo Alberto, Mazzei, Lorenzo, and Andreini, Antonio

Subjects
LEAN combustion, LATIN hypercube sampling, COMPUTATIONAL fluid dynamics, POLYNOMIAL approximation, MONTE Carlo method
Abstract

1-D codes are still much utilized regarding the preliminary design and the design phase of a single component. In particular, combustors is one of the most critical component in gas turbine engine and its design mainly affects all other components, starting from the turbine. During the initial phases of the design process, parameters are known approximately; during this phase, critical for the definition of a starting set of design parameters, there is little point in doing high fidelity Computational Fluid Dynamics (CFD) analyses. On the contrary, the exploration of the whole space is extremely important to better understand the behaviour of the system and to focus on the design objectives. Uncertainty quantification (UQ), mainly developed in recent years and applied in many fields, can be really helpful in the preliminary design phase and also as a support during the whole design process. This work comes with the idea to estimate the main source of geometrical uncertainties in the design phase of a combustor. The test case is based on a full annular lean burn combustor, tested at Central Institute of Aviation Motors (CIAM) during the LEMCOTEC (Low EMissions COre-engine TEChnologies) European project. Among the test points investigated in the experimental campaign, the Approach condition is analysed. The inner liner is considered to analyse the metal temperature. Therm-1D, a 1-D in-house simulation code, is used to model the combustor. After having built the baseline case of the combustor, several uncertainty analyses are investigated. In particular a classical Monte Carlo approach is compared with 4 innovative polynomial-chaos approaches for each group: Gauss quadrature, total order with Latin Hypercube sampling (LHS), probabilistic collocation and stochastic collocation. The analyses proved how the last two methods give the same results with a sensible lower amount of simulation (depending on the number of input variables). An additional sensitivity analysis to the order of the polynomial is conducted and results show how, with a 3rd order polynomial approximation, results can be very similar to those ones of the Monte Carlo simulation. Lastly, results are compared with experimental data to achieve a better understanding of the most relevant input parameters and the propagation of their uncertainty on the results. [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results