Computational Fluid Dynamics to Reach a High-ﬁdelity Simulator of

Performance in Rowing

Alban Leroyer

1 a

and Sophie Barré

Nantes Université, Ecole Centrale Nantes, CNRS, LHEEA, UMR 6598, F-44000 Nantes, France

CREPS Pays-de-la-Loire, 5 Avenue de la Babinière, 44240 La Chapelle-sur-Erdre, France

Keywords:

Rowing, Computational Fluid Dynamics (CFD), Numerical Simulation, High Performance Computing

(HPC), Fluid Dynamics, Fluid-Structure Interaction (FSI), Biomechanics, Performance Analysis.

Abstract:

The massive growth of computational power and the advances of the numerical models make the use of numer-

ical 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 mod-

elization 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 (Simulator of Performance in Rowing), 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.

1 INTRODUCTION

During the past twenty years, relationship between

Centrale Nantes, CREPS des Pays de la Loire and the

French Rowing Federation has been forged through

various research projects linked to performance sup-

port, including experimental campaign in towing tank

using speciﬁc devices to investigate the ﬂow physics

around hull and blades, on-the-water measurements,

and numerical validation. With both the upcoming

2024 Olympic and Paralympic Games in Paris and

the achievements in HPC and in CFD over the past

decade, time has come to capitalize all the knowledge

acquired to develop a high-ﬁdelity simulator of the

boat-rower(s)-oars system coupled with a CFD ﬂow

solver to better understand the physics of this complex

mechanical system. Preliminary results look very re-

alistic and promising, and illustrate the relevance of

some technical choices which were made. On this

basis and before being exploited, a deeper validation

step has to be carry out, which is a mandatory but not

straighforward prerequisite. The pursued objective of

this challenging tool is to help coaches and athletes in

the quest of the best performance.

https://orcid.org/0000-0001-5427-1082

2 MODEL OF THE GLOBAL

SYSTEM

BOAR-OAR(S)-ROWER(S)

The global system Boat-Oar(s)-Rower(s), denoted by

BOR system thereafter, is considered from a mechan-

ical and inertial point of view as a system composed

by a set of rigid bodies (even if deformed by the ﬂuid

loads and rower loads, the oars keep their inertia prop-

erties unchanged, which is a valid assumption). The

mass of the BOR system is conserved in time whereas

the global inertia properties (position of the center of

gravity and inertia matrix) changes according to the

internal degrees of freedom deﬁned by the position of

the oars and of the rower(s) with respect to the boat.

Once deﬁned the ﬂoating frame of reference linked

to the boat where the origin is set at the keel line at

the middle of the boat (see ﬁgure 1), the resolution of

the dynamics of the BOR system is then reduced to

the resolution of the Newton’s law of a system which

inertia properties changed in time and subjected to

external forces, similar to what is done in (Leroyer

and Visonneau, 2005). The external degrees of free-

dom (DOF) of the BOR system which are solved are

namely the position and the orientation of the hull as a

function of time: the hull kinematics is thus the output

Leroyer, A. and Barré, S.

Computational Fluid Dynamics to Reach a High-ﬁdelity Simulator of Performance in Rowing.

DOI: 10.5220/0011549300003321

In Proceedings of the 10th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2022), pages 117-124

ISBN: 978-989-758-610-1; ISSN: 2184-3201

117

Figure 1: Parameterization of the system.

of the resolution.

Except the gravity, the major part of the external

forces, strongly coupled to the BOR system, comes

from the hydrodynamic forces acting on the hull and

on the blades. To reach an high-ﬁdelity model, they

are computed using CFD (see section 3). Other aero-

dynamic forces which contributions are limited, are

taken into account through simpliﬁed analytical mod-

els. The motions of the rower(s) and of the oars

with respect to the boat represent the internal DOF

of the BOR system. They are responsible both for the

change of inertia parameters of the system and for the

propulsive force through the generated hydrodynamic

forces. Except the induced deformation of the shaft

which are solved together with the resolution of the

dynamics of the hull (Robert et al., 2018), all other

internal DOF are imposed to reproduce the variety

of kinematics found among elite rowers. Details are

given in section 4.

3 EXTERNAL HYDRODYNAMIC

LOADS AND COUPLING

3.1 Need for High-ﬁdelity CFD Model

More than two decades ago, experimental research

works were done to characterize ﬂows involved in

rowing, especially around the blade. A speciﬁc device

was designed and used in a towing tank to reproduce

the main characteristics of this violent ﬂow using real

oars. This initial goal was to better understand the

physics of this ﬂow and to build simpliﬁed models

(Barré, 1998; Barré and Kobus, 2010). Even if these

models can reproduce the right order of magnitude

and are useful to quickly test the simulator, they will

never be accurate enough to take into account all the

subtle interactions which appear during the propul-

sive phase, especially during the catch phase, which

is essential to the propulsive force generation of the

whole stroke. Given the high-accuracy requirement

to address sensibility analysis to small variations of

parameters, it was decided to directly couple the high-

ﬁdelity model based on CFD computations using the

ISIS-CFD solver to power the ﬂuid forces interacting

with the BOR system (on the hull and on the blade),

without any compromise in accuracy (Robert et al.,

2018). In this challenging task, this unique experi-

mental database turned out to be of great help to vali-

date the CFD tool (Robert, 2017; Robert et al., 2018).

Such a work has never been reported in the literature.

(Formaggia et al., 2009) coupled a simpliﬁed 6 DOF

model of the BOR system with a CFD ﬂow resolu-

tion around the hull while imposing analytical law of

ﬂuid loads for the blades. (Sliasas and Tullis, 2009;

Sliasas and Tullis, 2010) coupled a 1 DOF model of

the BOR system with CFD ﬂow resolution around the

blade (without taking into account either vertical mo-

tion or shaft ﬂexibility) and while using an analyti-

cal drag-based hull velocity model: results exposed

in (Sliasas and Tullis, 2009) seem somehow nonphys-

ical, especially when the propulsive force become un-

expectedly negative at the end of the drive phase but

which, oddly enough, does not appear in (Sliasas and

Tullis, 2010). Shaft ﬂexibility is investigated sepa-

rately without coupling with a BOR system in (Sliasas

and Tullis, 2011) , but using a one-way coupling then

requiring to repeat several times the simulation to

converge.

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118

3.2 ISIS-CFD Solver

ISIS-CFD is an incompressible unsteady Reynolds-

Averaged Navier–Stokes (RANS) solver devel-

oped by ECN-CNRS, available as a part of the

FINE

T M

/Marine computing suite which is dedicated

to marine applications. This solver is based on a

cell-centered unstructured ﬁnite-volume method. Ad-

vanced capabilities such as ﬂuid structure interac-

tion, automatic adaptive grid reﬁnement technique

and overlapping grid technology are required in the

present context to achieve both accurate and efﬁcient

computations. This worldwide used tool has been val-

idated through various CFD workshops in ship hy-

drodynamics (Deng et al., 2012; Deng et al., 2015;

Queutey et al., 2021). As previously mentionned, a

hard validation work has also been carried out for the

speciﬁc ﬂows involved in rowing using previous ex-

perimental research works on this topic (Barré, 1998;

Barré and Kobus, 2010; Robert, 2017; Robert et al.,

2018).

3.3 Co-simulation between ISIS-CFD

and SPRing

To reach an efﬁcient and robust algorithm for this

partitioned approach, the coupling iteration is done

within the non-linear iterations of the ﬂuid solver.

This implicit internal coupling solves both the dynam-

ics of the hull and the deformation of the oar shafts

computed by SPRing (Simulator of Performance in

Rowing) using the current ﬂuid loads and then update

these kinematic modiﬁcations to the ﬂuid part. The

model of ﬂexibility of the shaft is based on a beam

model with a variable stiffness law along its length.

The parameters of this law has been calibrated using

a speciﬁc ﬂexibility test bench for oars, which is not

described here.

Data transfer between the two codes is done

through a TCP/IP socket-based protocol, using the

ZeroMQ distributed message library (Akgul, 2013).

As other ﬂuid-structure interaction in hydrodynamics,

a stabilization procedure based on an artiﬁcial added

mass method is used to tackle the destabilising added-

mass effects (Yvin et al., 2018).

4 CONTROL OF THE INTERNAL

DOF

As previously described, the internal DOF of the BOR

system are given by the position of the oar and the

position of the rower. In most of research works in-

volving a model of rower, a kinematic control (time

Figure 2: Force as a function of sweep angle, raw data and

averaged procedure.

law of join angle (Cabrera and Ruina, 2006; Rongère

et al., 2011) or a dynamics control (time law of join

torque (Pettersson et al., 2014)) is used to drive the

posture of the rower in time and induce the motion of

the oar. Here it is done in the opposite way: the sweep

angle of the oars as well as the vertical height of the

blade with respect to the water surface are imposed in

time. The main motivation of this choice is the ease to

reproduce real crews since the sweep angle is a data

which is available at each on-the-water measurement.

It also offers the possibity to modify the rowing stroke

by modifying the temporal law of the sweep angle.

The vertical position of the blade is far much tricky

to track in-situ. At that time, this data is not yet mea-

sured. However, thanks to video analysis, a paramet-

ric model can be built to reproduce as accurate as pos-

sible the real path, as described in section 4.2. Tech-

nical gesture which identiﬁed each rower, called here

gesture signature, is reproduced through some para-

metric curves driving the athlete position.

4.1 Oar Motion Input

Sweep angle is given as a time series. When dealing

with an on-the-water measurement during a sequence

at constant stroke rate, an averaging procedure is ap-

plied to work with a pure periodic signal, see ﬁgure 2.

A synthesis procedure has been developed too, which

enable to create a B-spline based parametric model of

the sweep angle and then to play with the parameters

to modify the rowing stroke. An illustation of this

fonctionnality is given in section 5.

Vertical position of the blade is modeled by a set

of 16 parameters for the whole stroke: twelve of them

are dedicated to deﬁne both the catch phase (see ﬁg-

ure 3) and the release phase (see ﬁgure 4). The oth-

ers are dedicated to model the link between these two

phases.

Computational Fluid Dynamics to Reach a High-ﬁdelity Simulator of Performance in Rowing

119

blade

air

water

trajectory of the

blade bottom

min

∆t

∆θ

Figure 3: Parametrization of the vertical motion of the blade

during the catch phase.

: blade height with respect to the water at the begin-

ning of the catch phase,

∆θ

: starting sweep angle of the catch phase minus

minimum sweep angle,

min

:blade height at minimum sweep angle,

∆θ

: angular deviation before immersion,

∆t

: immersion time,

α: slope of immersion at the end of the catch phase. of

immersion.

4.2 Rower Gesture Signature

Even if this is not what’s happen in real life, the whole

motion of oars is imposed using the input data and

model described previously, and the rower moves ac-

cordingly. However, for a given position of oars,

there is multiple rower positions satisfying the con-

straints (hands attached to handles, foot attached to

the stretcher and buttocks following the sliding seat

path). The temporal evolution of limbs position sat-

isfying the constaints deﬁne the gesture signature. To

reproduce a variety of gesture signatures, evolution

of legs bending, trunk inclination and arms bend-

ing are driven through parametric curves, function of

their respective stretching ratio at the current posture.

The temporal evolution of the rower position is then

computed incrementally : at each new time step, the

stretching ratio are used to ﬁnd the contribution of

each part of the rower body involved in the oar po-

sition increment, as shown in the ﬁgure 5 at the left.

The resulting rower kinematics is shown at the right.

As an illustration, it can be seen that at the end of the

driving phase as well as the beginning of the recov-

ery, arms are the limbs which is mainly responsible

of the oar motion whereas around the catch phase, the

oar motion is mainly due to the modiﬁcation of legs

bending. Some additional features, such as spine cur-

water

air

trajectory of the

blade bottom

max

∆t

∆θ

Figure 4: Parametrization of the vertical motion of the blade

during the release phase.

∆t

: exit time,

β: slope of exit at the beginning of release phase

∆θ

: angular deviation after exit,

max

:blade height at maximum sweep angle,

: blade height with respect to the water at the end of

the release phase,

∆θ

: maximum sweep angle minus ﬁnal sweep angle

of the release phase.

vature, lift of the heels and strech of the shoulders

close to the catch, have also be implemented to enrich

the gesture signature. Special care need to be done

for the whole kinematics which is imposed. Sweep

angles, blades height and limbs motion have to be de-

ﬁned with smooth enough temporal series (equivalent

to functions of class C

) to obtain a smooth response

of the hull dynamics. Other input data as density and

geometry of each members of the rower are mainly

based on the work described in (Yeadon, 1990) and

(Leva, 1996), and set them according to the rower

morphology.

5 RESULTS

A ﬁrst preliminary simulations have been carried out

using a quite coarse mesh of around 2 million cells

to provide a proof-of-concept. An initial phase starts

with a speed-up of the hull using an imposed for-

ward velocity ramp up to a guess velocity while keep-

ing solving heave and pitch (see ﬁgure 6). In the

meanwhile, the rower moves from his initial position

(sweep angle 90°, fully stretched legs, vertical trunk)

to the catch phase with a smooth kinematic connec-

tion at the conﬁguration when the height of the blade

reaches its maximum value. This connection is done

at the time when the whole resolution of the dynamics

starts.

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120

Figure 5: On the top left, contribution of legs bending, trunk inclination and arms bending to the oar rotation as a function

of time, resulting on the incremental procedure. On the bottom left, averaged sweep angle as a function of time coming from

measurement. On the right, resulting sequence of rower postures for a whole rowing stroke.

Figure 6: Imposed sweep angle as a function of time includ-

ing the initial phase.

A speciﬁc Graphical User Interface (GUI) has

been developed to set all the input parameters, from

the generation of the periodic sweep angle to the mor-

phology of each rower (see ﬁgure 7).

Figure 7: SPRing input GUI showing the synthesis of a

given sweep angle with B-spline.

Considering the ﬁnal objective of the tool, an

important aspect of the project concerns the realis-

tic rendering of the simulation, see ﬁgure 8. It has

Figure 8: Realistic rendering of the co-simulation

SPRing/ISIS-CFD. Computed free surface are imported

from CFD simulation as STL triangulation.

Computational Fluid Dynamics to Reach a High-ﬁdelity Simulator of Performance in Rowing

121

Figure 9: Stickman rendering of a double skull with a co-simulation SPRing/ISIS-CFD. Morphology and gesture signature

can be deﬁned speciﬁcally for each rower.

been developed using the open-source Blender soft-

ware (Community, 2018). Such a post-treatment is

important as a communication facility with coaches,

but also to easier confront the reality with the simula-

tion. However, due to the complexity to ﬁt automat-

ically the human mesh with the morphology of each

rower, this approach has been put aside at the moment

in favor of a less realistic "stickman" representation

of rowers, see ﬁgures 9 and 10. This fully automatic

mode, still using Blender, is driven through another

dedicated GUI. Speciﬁc outputs (hull kinematics, oar

deformation, incident velocity around the blade,...)

can be visualized through synchronized graphs or by

superimposed arrows in a multi-view scene, which

greatly facilitate the physical analysis too.

6 CONCLUSION AND

PERSPECTIVE

To achieve the scientiﬁc challenge of a high-ﬁdelity

model of the BOR system, a multi-body system has

been developed to accurately model the kinematics of

the rower with respect to its environment. It is the next

step after previously published reference CFD results

(Robert et al., 2018; Robert et al., 2014), on which

it relies. This imposed kinematics is driven both by

some gesture parameters and time evolution of sweep

angle of each oar (which can be provided from in-situ

data measurements) and height of the blade with re-

spect to the free surface. The dynamics of the global

system is then reduced to the dynamics of the hull,

which is solved by integrating the major ﬂuid forces

acting on both the hull and the blades through CFD

resolution (Robert et al., 2018; Robert et al., 2014).

The original kinematic approach of the control of the

gesture signature has been motivated to be in line with

the objective to have both an accurate description and

the most operational tool to analyse and serve perfor-

mance in rowing. Before playing this role, an exten-

sive step of validation for these coupled simulations

needs to be investigated using on the water measure-

ment. This validation step is crucial because Science

can bring a new insight on sport performance, only if

the model lives up to the expectations in term of ac-

curacy so that the physical analysis of the phenom-

ena can be trustingly carried out. The preliminary

results obtained with the high-ﬁdelity co-simulation

look very realistic and is a source of motivation to pur-

sue this path. In parallel of that, more advanced me-

chanical analysis (load on human joints, power con-

sumption, efﬁciency,. . . ) is planned to beneﬁt from all

the data which are computed. At term, such tool tar-

gets to bring objective and unbiased criteria for ques-

tions which have only empirical answer up to now.

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122

Figure 10: Multi-view stickman rendering, synchronized with the graph showing the sweep angle as a function of time, made

by the SPRing output GUI.

ACKNOWLEDGEMENTS

We would like to thank the students of the "Scientiﬁc

Challenge 2024" project of Centrale Nantes who are

participated to the development of SPRing. This work

beneﬁts from HPC resources of ICI-CNSC through

the call GLICI/2018. It is also granted access to the

HPC resources of GENCI-CINES under the alloca-

tion A10856 made by GENCI.

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