A compressible multiphase flow model for violent aerated wave impact problems

This paper focuses on the numerical modelling of wave impact events under air entrapment and aeration effects. The underlying flow model treats the dispersed water wave as a compressible mixture of air and water with homogeneous material properties. The corresponding mathematical equations are based on a multiphase flow model which builds on the conservation laws of mass, momentum and energy as well as the gas-phase volume fraction advection equation. A high-order finite volume scheme based on monotone upstream-centred schemes for conservation law reconstruction is used to discretize the integral form of the governing equations. The numerical flux across a mesh cell face is estimated by means of the HLLC approximate Riemann solver. A third-order total variation diminishing Runge–Kutta scheme is adopted to obtain a time-accurate solution. The present model provides an effective way to deal with the compressibility of air and water–air mixtures. Several test cases have been calculated using the present approach, including a gravity-induced liquid piston, free drop of a water column in a closed tank, water–air shock tubes, slamming of a flat plate into still pure and aerated water and a plunging wave impact at a vertical wall. The obtained results agree well with experiments, exact solutions and other numerical computations. This demonstrates the potential of the current method to tackle more general wave–air–structure interaction problems

Proton stripping induced by 13^{13}C at 50 Mev/nucleon on 12^{12}C, 40^{40}Ca and 58^{58}Ni

The article looks back on the 183 workshops and briefing sessions of the 15 annual UKSG conferences from 1990 to 2004. The content of this particular form of professional training reflects the development of the professional environment, interests and activities of librarians, especially the emergence of the digital library. Nine major subjects of information and debate are identified: human resource management, new software, acquisition of e-serials, legal aspects, emerging standards, usage statistics, library/vendor relationship, document delivery, and publishing. An analysis of attendance and some remarks on specific features of the sessions complete this “historical study”

Development and Validation of a Partitioned Fluid-Structure Solver for Transonic Panel Flutter with Focus on Boundary Layer Effects

A partitioned fluid-structure coupling code for transonic panel flutter has been developed and validated. The Reynolds-averaged Navier-Stokes equations are solved numerically by means of an implicit finite volume method to account for nonlinear aerodynamics, as there are shock waves and a viscous boundary layer at the panel surface. An implicit finite element formulation of the structural equations as well as a Galerkin solution of the von-Kármán plate equation are employed to solve elastic panel deformations with respect to geometric nonlinearities. A detailed validation process is presented in this paper for high subsonic and low supersonic Mach numbers. This comprises a discussion of available results from literature with the objective to propose a guideline for validation purposes of partitioned panel flutter solvers. Thereupon the code is used for studies on the impact of turbulent boundary layer characteristics on aeroelastic stability boundaries and post-flutter. An evaluation of flutter modes and frequencies in the post-flutter domain as well as a discussion of the corresponding flow phenomena is presented

A Priori Neural Networks Versus A Posteriori MOOD Loop: A High Accurate 1D FV Scheme Testing Bed

In this work we present an attempt to replace an a posteriori MOOD loop used in a high accurate Finite Volume (FV) scheme by a trained artificial Neural Network (NN). The MOOD loop, by decrementing the reconstruction polynomial degrees, ensures accuracy, essentially non-oscillatory, robustness properties and preserves physical features. Indeed it replaces the classical a priori limiting strategy by an a posteriori troubled cell detection, supplemented with a local time-step re-computation using a lower order FV scheme (ie lower polynomial degree reconstructions). We have trained shallow NNs made of only two so-called hidden layers and few perceptrons which a priori produces an educated guess (classification) of the appropriate polynomial degree to be used in a given cell knowing the physical and numerical states in its vicinity. We present a proof of concept in 1D. The strategy to train and use such NNs is described on several 1D toy models: scalar advection and Burgers' equation, the isentropic Euler and radiative M1 systems. Each toy model brings new difficulties which are enlightened on the obtained numerical solutions. On these toy models, and for the proposed test cases, we observe that an artificial NN can be trained and substituted to the a posteriori MOOD loop in mimicking the numerical admissibility criteria and predicting the appropriate polynomial degree to be employed safely. The physical admissibility criteria is however still dealt with the a posteriori MOOD loop. Constructing a valid training data set is of paramount importance, but once available, the numerical scheme supplemented with NN produces promising results in this 1D setting. Keywords Neural network • Machine learning • Finite Volume scheme • High accuracy • Hyperbolic system • a posteriori MOOD. Mathematics Subject Classification (2010) 65M08 • 65A04 • 65Z05 • 85A2