Numerical Algorithms for Divergence-Free Velocity Applications

This work focuses on the well-known issue of mass conservation in the context of the finite element technique for computational fluid dynamic simulations. Specifically, non-conventional finite element families for solving Navier–Stokes equations are investigated to address the mathematical constraint of incompressible flows. Raviart–Thomas finite elements are employed for the achievement of a discrete free-divergence velocity. In particular, the proposed algorithm projects the velocity field into the discrete free-divergence space by using the lowest-order Raviart–Thomas element. This decomposition is applied in the context of the projection method, a numerical algorithm employed for solving Navier–Stokes equations. Numerical examples validate the approach’s effectiveness, considering different types of computational grids. Additionally, the presented paper considers an interface advection problem using marker approximation in the context of multiphase flow simulations. Numerical tests, equipped with an analytical velocity field for the surface advection, are presented to compare exact and non-exact divergence-free velocity interpolation

Computing Interface Curvature from Height Functions Using Machine Learning with a Symmetry-Preserving Approach for Two-Phase Simulations

The volume of fluid (VOF) method is a popular technique for the direct numerical simulations of flows involving immiscible fluids. A discrete volume fraction field evolving in time represents the interface, in particular, to compute its geometric properties. The height function method (HF) is based on the volume fraction field, and its estimate of the interface curvature converges with second-order accuracy with grid refinement. Data-driven methods have been recently proposed as an alternative to computing the curvature, with particular consideration for a well-balanced input data set generation and symmetry preservation. In the present work, a two-layer feed-forward neural network is trained on an input data set generated from the height function data instead of the volume fraction field. The symmetries for rotations and reflections and the anti-symmetry for phase swapping have been considered to reduce the number of input parameters. The neural network can efficiently predict the local interface curvature by establishing a correlation between curvature and height function values. We compare the trained neural network to the standard height function method to assess its performance and robustness. However, it is worth noting that while the height function method scales perfectly with a quadratic slope, the machine learning prediction does not

Numerical Coupling between a FEM Code and the FVM Code OpenFOAM Using the MED Library

This paper investigates a numerical code-coupling technique to tackle multiphysics and multiscale simulations using state-of-the-art software packages that typically address some specific modeling domain. The coupling considers the in-house FEM code FEMuS and the FVM code OpenFOAM by exploiting the MED library from the SALOME platform. The present approach is tested on a buoyancy-driven fluid flow within a square cavity, where the buoyancy force constitutes the coupling term. In uncoupled scenarios, momentum and temperature equations are solved in both FEM and FVM codes without data exchange. In the coupled setting, only the OpenFOAM velocity and the FEMuS temperature fields are solved separately and shared at each time step (or vice versa). The MED library handles the coupling with ad hoc data structures that perform the field transfer between codes. Different Rayleigh numbers are investigated, comparing the outcomes of coupled and uncoupled cases with the reference literature results. Additionally, a boundary data transfer application is presented to extend the capabilities of the coupling algorithm to coupled applications with separate domains. In this problem, the two domains share interfaces and boundary values on specific fields as fluxes are exchanged between the two numerical codes

Use of Pt/CexZr1-xAl2O3 as Advanced Catalyst for Hydrogen Peroxide Thrusters

The capability of different Pt/Ce0.6Zr0.4/Al2O3 catalytic systems of effectively decomposing H2O2 has been studied in view of their application to monopropellant thrusters. BET surface area measurements, X-Ray Diffractometry (XRD) and Scanning Electron Microscopy (SEM) have been used together with catalytic tests in order to evaluate the advantages of using CeO2-ZrO2 mixed oxide solid solution as an alternative to current three ways catalysts (TWCs). From the assessment of alternative solutions, a Pt/Ce0.6Zr0.4/Al2O3 catalyst suitable to effectively decompose H2O2 has been identified. SEM-EDX analyses ruled out the occurrence of phase segregation and selective deposition of Pt on Zr during the catalyst preparation. No changes in the crystalline arrangement of the catalyst samples after H2O2 decomposition have been detected by XRD measurements, except for a slight crystallization or grain size growth as a consequence of the high temperatures experienced during the reaction...

Post-Reaction Characterization of a Pt/CexZrx-1/Al2O3 Catalyst after the Use in a HTP Thruster

This work illustrates the comparison of the physical and chemical characterization of two catalytic beds supporting Pt on Al2O3 with and without an intermediate thin film of mixed Cerium-Zirconium oxide, to be used for H2O2 decomposition in rocket propulsion applications. The characterizations have been carried out by XRD, SEM and EDX analyses before and after conducting propulsive performance tests in a laboratory rocket engine prototype using commercial HTP (High Thrust Peroxide) propellant

Public Preferences for Investments in Renewable Energy Production and Energy Efficiency

In this paper we investigate the choices citizens make when asked to express willingness to support a proposed energy policy and are then compelled to allocate the program funds to either renewable energy or energy efficiency. In a survey study based on a random sample of residents of the state of Maine, USA, we find that citizens have preferences for specific types of renewable energy but these preferences do not yield significantly different allocation of investment funds between renewable energy and energy efficiency. We find that preferences are generally consistent regardless of presentation of options (i.e. limited ordering effects). Our results also indicate that personal characteristics that are understudied in the energy literature, including promotion/prevention focus and social/fiscal leanings, influence both willingness to support energy policies and also their allocation of fund choices, but in different ways. This suggests the importance of including multiple options in energy policy proposals, and that targeted messages regarding the components of such policies is key for optimal communication