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International audience; The massive growth of computational power and the advances of the numerical models make the use of numerical simulations to help analysing and improving sport performance achievable. However, it is still challenging because the physical configurations generally involved complex coupled problems and because human is part of the system. Furthermore, elite athletes already operate near an optimal point. As a consequence, the modelisation of all the phenomena that come into play has to be accurate enough to be useful and relevant when the objective is to analyse interactions and to give reliable trends while varying some parameters. The case of rowing is presented here, through the development of SPRing, a high-fidelity simulator of the global system "boat-oars-rower(s)" coupled with the resolution of the Reynolds-Averaged Navier-Stokes equations to provide fluid forces acting on it.

International audience; One main concern in climate science is to reduce uncertainties on sea level predictions. In particular,these uncertainties depend on the quantification of the mass losses of polar ice caps including Greenlandice sheet. Iceberg calving at Greenland glaciers accounts for up to half of ice losses at glacier termini.Some icebergs detaching from Greenland glaciers have the height of the glacier and are thin so unstableand capsize exerting a force on the glacier front which generates a seismic wave recorded at stationsin Greenland and further away. These seismic signals have been recorded for the last twenty-five yearsby the permanent stations. The number of such events has increased and the spatial distribution hasevolved. What is the evolution of the volume of capsizing icebergs ?Field data on capsizing icebergs are lacking, except for seismic data. Indeed, the database of seismicsignals gives continuous information about iceberg capsize events: we aim to extract information fromthese data. The characteristics of seismic signals depend on the iceberg volume and the whole dynamicof the capsizing iceberg [2]. The global aim of this work is to calculate the volum of capsizing icebergs.To do this, we compare recorded seismic signals to synthetic seismic signals calculated using a modelof iceberg capsize. Therefore, we solve an inverse problem to obtain information on the dynamic of thecapsize and in particular an estimation of the volume of the iceberg.Iceberg capsize dynamics depends on complex phenomena: iceberg-water interactions, iceberg-glacierfriction, glacier-sea floor friction, elasto-viscoplastic deformation of ice. Solving directly fluid flow, solidmotion, and contact equations even in two dimensions is very costly and can hardly be used to generatecatalogs and to solve inverse problem. Therefore, a simplified mechanical model of a capsizing icebergin water has been developed based on few assumptions. The proposed model, named SAFIM (semi-analytical floating iceberg model) accounts for sea hydrodynamics only through hydrostatic pressure,pressure drag, and added mass, and it has been validated based on a separate state-of-the-art Compu-tational Fluid Dynamics code which can handle free surface and arbitrary iceberg configurations. Theerror on the horizontal force exerted by the fluid on the iceberg capsizing in open ocean (with no contactwith the glacier) calculated with SAFIM goes from 4% to 20%, and the drag coefficient that minimizesthis error goes from 1 to 3, depending on the aspect ratio of the iceberg.; Une des questions principales en sciences du climat est l'amélioration de la précision des prédictions du niveau des océans. En particulier, les incertitudes dépendent de la quantification de la perte de masse des calottes polaires comme celle du Groenland. Le détachement d'icebergs au front des glaciers du Groenland représente jusqu'à la moitié des pertes de masses aux terminus de ces glaciers. Certains icebergs qui se détachent des glaciers du Groenland ont la même hauteur que le terminus du glacier, sont fins donc instables et se retournent proche du glacier. Ils exercent ainsi une force sur le glacier qui crée un signal sismique enregistré sur des stations situées au Groenland ou plus loin. Le nombre d'évènements de ce type a augmenté et la distribution spatiale de ces évènements a évoluée [1]. Qu'en est-il de l'évolution du volume de ces icebergs qui se retournent ? Les données de terrain sur ces évènements sont rares, à l'exception des données sismiques qui donnent des informations continues sur les évènements de retournement d'icebergs. Les caractéristiques du signal sismique dépendent du volume de l'iceberg et de toute la dynamique du retournement [2]. Le but ultime de ce travail est de calculer les volumes des icebergs qui se retournent. Pour ce faire, nous comparons des signaux sismiques enregistrés à des signaux sismiques synthétiques obtenus à l'aide d'un modèle mécanique de retournement d'iceberg [3]. Ainsi, nous résolvons un problème inverse pour obtenir des informations sur la dynamique du retournement et une estimation du volume des icebergs [4]. Nous nous intéressons, dans cet article, à la modélisation mécanique du retournement d'un iceberg. La dynamique du retournement d’un iceberg dépend de phénomènes complexes : les interactions entre l’iceberg, l’eau et le glacier, le frottement entre le glacier et le lit rocheux, la déformation élasto-viscoplastique de la glace. Résoudre les équations de la mécanique des fluides, du mouvement solide et les équations de contacts solides est très coûteux, même en deux dimensions, et peu adapté à la génération d’un catalogue pour résoudre un problème inverse. Ainsi, un modèle mécanique simplifié de retournement d’iceberg dans l’eau a été développé sous quelques hypothèses. Le modèle proposé, nommé SAFIM (modèle semi-analytique d’un iceberg flottant) tient compte de la dynamique des fluides via la pressionhydrostatique, une pression de traînée et des masses ajoutées. Ce modèle SAFIM a été validé à partird’un modèle de mécanique des fluides numérique ISIS-CFD qui résoud la dynamique d’un fluide à surface libre en interaction avec un solide. L’erreur sur la force horizontale du fluide sur l’iceberg qui se retourne dans un océan infini (sans contact avec le glacier) calculée avec SAFIM est de l’ordre de 4% à 20%, et le coefficient de traînée qui minimise cette erreur varie entre 1 et 3, en fonction du rapport d’aspect de l’iceberg.

One main concern in climate science is to reduce uncertainties on sea level predictions. In particular,these uncertainties depend on the quantification of the mass losses of polar ice caps including Greenlandice sheet. Iceberg calving at Greenland glaciers accounts for up to half of ice losses at glacier termini.Some icebergs detaching from Greenland glaciers have the height of the glacier and are thin so unstableand capsize exerting a force on the glacier front which generates a seismic wave recorded at stationsin Greenland and further away. These seismic signals have been recorded for the last twenty-five yearsby the permanent stations. The number of such events has increased and the spatial distribution hasevolved. What is the evolution of the volume of capsizing icebergs ?Field data on capsizing icebergs are lacking, except for seismic data. Indeed, the database of seismicsignals gives continuous information about iceberg capsize events: we aim to extract information fromthese data. The characteristics of seismic signals depend on the iceberg volume and the whole dynamicof the capsizing iceberg [2]. The global aim of this work is to calculate the volum of capsizing icebergs.To do this, we compare recorded seismic signals to synthetic seismic signals calculated using a modelof iceberg capsize. Therefore, we solve an inverse problem to obtain information on the dynamic of thecapsize and in particular an estimation of the volume of the iceberg.Iceberg capsize dynamics depends on complex phenomena: iceberg-water interactions, iceberg-glacierfriction, glacier-sea floor friction, elasto-viscoplastic deformation of ice. Solving directly fluid flow, solidmotion, and contact equations even in two dimensions is very costly and can hardly be used to generatecatalogs and to solve inverse problem. Therefore, a simplified mechanical model of a capsizing icebergin water has been developed based on few assumptions. The proposed model, named SAFIM (semi-analytical floating iceberg model) accounts for sea hydrodynamics only through hydrostatic pressure,pressure drag, and added mass, and it has been validated based on a separate state-of-the-art Compu-tational Fluid Dynamics code which can handle free surface and arbitrary iceberg configurations. Theerror on the horizontal force exerted by the fluid on the iceberg capsizing in open ocean (with no contactwith the glacier) calculated with SAFIM goes from 4% to 20%, and the drag coefficient that minimizesthis error goes from 1 to 3, depending on the aspect ratio of the iceberg., Une des questions principales en sciences du climat est l'amélioration de la précision des prédictions du niveau des océans. En particulier, les incertitudes dépendent de la quantification de la perte de masse des calottes polaires comme celle du Groenland. Le détachement d'icebergs au front des glaciers du Groenland représente jusqu'à la moitié des pertes de masses aux terminus de ces glaciers. Certains icebergs qui se détachent des glaciers du Groenland ont la même hauteur que le terminus du glacier, sont fins donc instables et se retournent proche du glacier. Ils exercent ainsi une force sur le glacier qui crée un signal sismique enregistré sur des stations situées au Groenland ou plus loin. Le nombre d'évènements de ce type a augmenté et la distribution spatiale de ces évènements a évoluée [1]. Qu'en est-il de l'évolution du volume de ces icebergs qui se retournent ? Les données de terrain sur ces évènements sont rares, à l'exception des données sismiques qui donnent des informations continues sur les évènements de retournement d'icebergs. Les caractéristiques du signal sismique dépendent du volume de l'iceberg et de toute la dynamique du retournement [2]. Le but ultime de ce travail est de calculer les volumes des icebergs qui se retournent. Pour ce faire, nous comparons des signaux sismiques enregistrés à des signaux sismiques synthétiques obtenus à l'aide d'un modèle mécanique de retournement d'iceberg [3]. Ainsi, nous résolvons un problème inverse pour obtenir des informations sur la dynamique du retournement et une estimation du volume des icebergs [4]. Nous nous intéressons, dans cet article, à la modélisation mécanique du retournement d'un iceberg. La dynamique du retournement d’un iceberg dépend de phénomènes complexes : les interactions entre l’iceberg, l’eau et le glacier, le frottement entre le glacier et le lit rocheux, la déformation élasto-viscoplastique de la glace. Résoudre les équations de la mécanique des fluides, du mouvement solide et les équations de contacts solides est très coûteux, même en deux dimensions, et peu adapté à la génération d’un catalogue pour résoudre un problème inverse. Ainsi, un modèle mécanique simplifié de retournement d’iceberg dans l’eau a été développé sous quelques hypothèses. Le modèle proposé, nommé SAFIM (modèle semi-analytique d’un iceberg flottant) tient compte de la dynamique des fluides via la pressionhydrostatique, une pression de traînée et des masses ajoutées. Ce modèle SAFIM a été validé à partird’un modèle de mécanique des fluides numérique ISIS-CFD qui résoud la dynamique d’un fluide à surface libre en interaction avec un solide. L’erreur sur la force horizontale du fluide sur l’iceberg qui se retourne dans un océan infini (sans contact avec le glacier) calculée avec SAFIM est de l’ordre de 4% à 20%, et le coefficient de traînée qui minimise cette erreur varie entre 1 et 3, en fonction du rapport d’aspect de l’iceberg.

International audience; CFD can be considered as a mature tool now for steady state ship hydrodynamic applications such as resistance in calm water. Accurate enough predictions can be obtained with reasonable resources even for fully appended hulls, both for model and for full scale in a routine design procedure. Compared with resistance computation, validation for propulsion computation is much more challenging. To author's knowledge, the only approach that is capable to predict ship propulsion with accuracy is to simulate directly the rotating propeller with sliding grid or overset approach. Time accurate simulation is required for such simulation even if time averaged solution is sufficient. Our experience from V&V exercises is that a reliable numerical uncertainty estimation is nearly impossible for such simulation due to high iterative error as well as time discretization error. A reduced model as a possible cost effective solution is to operate the propeller in a rotating reference frame: in order to model a general rigid-body motion (rotation) of one object, the RANS equations are solved in the moving reference frame but written in terms of absolute or inertial reference frame quantities. In the simplest case of a propeller in open-water, that rotating frame method (RFM) allows the use of steady calculations. Nevertheless, unsteady flows are possible to be modelled with this RFM approach, for instance when the unsteadiness comes from the flow pattern (vortex shedding, cavitation, etc.) or even for some cases of imposed motions (roll-damping). A third solution for self propulsion simulation is to model the effect of propeller by simple body forces in the RANS solver (Actuator Disk or AD) where propeller thrust is provided by the RANS solver. The case considered is the ONRT Tumblehome ship, Figure 1, from the Tokyo 2015 Workshop on CFD. It is a very challenging case for computation as it involves 6-DOF free motions with active rudder control in waves to maintain a straight course. In any case the overset technique is used to handle rudder motion and the sliding grid approach for the propeller resolved simulations. Here the use of RFM is justified as soon as the propellers are located far from the hull. Results obtained with our ISIS-CFD flow solver are briefly summarized in Figure 2. It compares the effect of the propeller representation on the predicted ship speed in waves: from propeller resolved, to propeller modelled (AD) and to RFM model. EFD conditions have been fulfilled with CFD simulations initialized from self-propulsion in calm water. If the computational cost goes from 2 to 3 days with the propeller modelled or RFM and until 30 days for the propeller resolved, the time-averaged computed ship speed difference with EFD is about 0.7% with RFM or propeller resolved and only 2.4% with AD approach. However, in any case and for reasons as yet unknown, the amplitude of the signal is always underestimated by a factor of 2. Figure 1: Propulsive system of the ONRT Tumblehome. Figure 2: Time history of the normalized ship speed with three CFD approaches for propeller and comparison with EFD.

One of the challenges of simulating free-surface flow around moored or floating objects is the modelling, including the algorithmic coupling, of objects whose dynamics is determined by a two-way interaction with the incoming waves. The ’traditional’ way of numerically coupling the flow dynamics with the dynamics of a floating object becomes unstable (or requires severe underrelaxation) when the added mass is larger than the mass of the object. To deal with this two-way interaction, a more simultaneous type of numerical coupling is being developed. The quasi-simultaneous method will be demonstrated on a number of simulation results for engineering applications from the offshore industry: the motion of a moored TLP platform in extreme waves, and the launch of a free-fall life boat.

An overview is presented of the activities conducted within the NATO STO Task Group AVT-204 to “Assess the Ability to Optimize Hull Forms of Sea Vehicles for the Best Per- formance in a Sea Environment.” The objective is the development of a greater understanding of the potential and limitations of the hydrodynamic optimization tools. These include low- and high-fidelity solvers, automatic shape modification methods, and multi-objective optimiza- tion algorithms, and are limited here to a deterministic application. The approach includes simulation-based design optimization methods from different research teams. Analysis tools include potential flow and Reynolds-averaged Navier-Stokes equation solvers. Design modifica- tion tools include global modification functions, control point based methods, and parametric modelling by hull sections and basic curves. Optimization algorithms include particle swarm optimization, sequential quadratic programming, genetic and evolutionary algorithms. The ap- plication is the hull-form and propeller optimization of the DTMB 5415 model for significant conditions, based on actual missions at sea.

International audience

International audience; This paper presents a computational study of the flow around the Japan Bulk Carrier (JBC) with or without an Energy Saving Device (ESD) in front of the propeller. This study first conducted at model scale was performed in the framework of the Tokyo 2015 Workshop on Numerical Ship Hydrodynamics. Configurations with and without ESD, with and without propeller are compared and analysed. Conclusions about the efficiency of this specific ESD at model scale are drawn. A detailed analysis of the local flow for the configuration without duct is also conducted and recent results obtained by using hybrid LES turbulence closure are used to shed some light on the flow physics. Finally, this paper is complemented with an assessment of the efficiency of the same ESD at full scale which is provided to evaluate the influence of scale effects in this specific context.

International audience

International audience

International audience; This paper is devoted to the validation exercises with the ISIS-CFD code conducted for the test cases proposed for the MASHCON conference. CFD simulations have been performed for the 4 different pure yaw and pure sway test cases under shallow water condition. Predicted results are compared with the measurement data provided by FHR.

International audience; This paper is devoted to the verification and validation exercises with the ISIS-CFD code conducted for the Tokyo 2015 workshop. In addition to numerical uncertainty estimation, we also address issues such as the effect of turbulence modelization, wall resolved versus wall modeled approach, propeller resolved versus propeller modeled approach for self-propulsion simulation, etc.