Robust Numerical Formulations
for the Simulation ofMultiphase &Multicomponent Fluid
Flows in Naturally
Fractured, Heterogeneous& Anisotropic PorousMedia Using Unstructured
Meshes & Multiscale
Discretizations
Paulo R. M. Lyra &Darlan K. E. Carvalho
Deep Reinforcement Learningfor Optimal Flow Control
E. Hachem,
J. Viquerat,
A. Larcher &P. Meliga
A Primer on Mathematical
Modellingby Alfio Quarteroni &Paola Gervasio
Book Review
by Dan Givoli

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2 Robust Numerical Formulations for the Simulation of Multiphase& Multicomponent Fluid Flows in Naturally Fractured, Heterogeneous& Anisotropic Porous Media Using Unstructured Meshes &Multiscale Discretizations
Paulo R. M. Lyra & Darlan K. E. Carvalho
10 Deep Reinforcement Learning for Optimal Flow Control
E. Hachem, J. Viquerat, A. Larcher & P. Meliga
16 A Primer on Mathematical Modelling
Alfio Quarteroni & Paola Gervasio
Book Review by Dan Givoli
17 KSCM

Korea

18 GIMC

Italy

20 ABMEC Brazil
22 AMCA

Argentina

24 USACM USA
26 GACM

Germany

28 JACMJapan

30 SEMNI

Spain

31 CEACM

Central Europe

32 JSCESJapan

33 Conference Diary Plannercontents

R obust N umerical F ormulations for the S imulation of
M ultiphase & M ulticomponent F luid F lows in N aturally
F ractured, H eterogeneous & A nisotropic P orous M edia
U sing U nstructured M eshes & M ultiscale D iscretizations
by
Paulo R. M. Lyra
paulo.lyra@ufpe.br
Darlan K. E. Carvalho
darlan.ecarvalho@ufpe.br
Mechanical Engineering
Department - CTG
Research Institute inPetroleum and Energy
LITPEG
Federal University of
Pernambuco, Brazil

he numerical modelling and simulationof multiphase and multicomponent flows
in naturally fractured porous media, such as
petroleum reservoirs, aquifers, geothermal
reservoirs, etc., for different industrial andenvironmental applications, including oil
and gas recovery, contaminant transport,geologic CO_2 sequestration, nuclear waste
disposal and geothermal energy production,
is a very challenging task. It involves themodelling of complex geological features
such as high rock heterogeneity andanisotropy, the presence of faults, fractures(channels and barriers), pinchouts or evencaves, cavities and vugs, that can beusually encountered in carbonate (karst)
reservoir rocks. Besides, in many oil
industry applications, it is also necessary
to deal with directional and/or multilateralwells due to the necessity to avoid wellsurface locations that are logistically or
environmentally inadequate. In addition,some problems may involve the couplingof thermal-hydro-mechanical and chemical
phenomena, which ideally requires integrated modelling of reservoirs, wells and facilities, and even, the numerical simulation for
multiple reservoirs. Despite the remarkableprogress in this field, in the last decades,
there is still a need for extensive research,ranging from the increase of physical complexity associated to the explosive growthof data from the geological characterization
to the introduction of use of general andflexible unstructured meshes to cope withgeometric complexity, the consideration of
uncertainty propagation and the inclusionof all components of High Performance
Computing (HPC) and data analytics [14;21; 31].

T

In this context, ideally, several propertiesare desired from the numerical formulations
used to discretize the PDEs that result
from the mathematical modelling of these
phenomena, such as: a solid theoreticalfoundation (e.g., stability, coercitivity,
convergence and error estimates); belocally conservative and satisfy a Discrete
Maximum Principle (DMP), to achieve
high-order of accuracy on general
iacm expressions 50/22 2

unstructured and non “K-orthogonal” grids;
produce solutions free of Grid Orientation
Effects (GOE); lead to well-conditioned linear systems; to incorporate HPC strategies,
etc. Unfortunately, currently, there is no
scheme that satisfies all these properties
simultaneously [16; 17].
Our research group: PADMEC (High
Performance Computing on Computational
Mechanics) has investigated, proposedand combined various components to develop robust, accurate and flexible reservoir
simulators that can address the above
challenges. In particular, here, we present
some recent developments related to
non-orthodox finite volume methods for thepressure equation, the use of very highorder methods for the transport equation,
the extension of multiscale and multilevel
locally conservative methods to unstructured
meshes and the modelling of multiphaseand multicomponent fluid flow in heterogeneous, anisotropic and naturally fractured
reservoirs.
Non-Orthodox Finite Volume Methods for
the Pressure Equation
Among the locally mass conservative linear
schemes typically used in the petroleum
reservoir simulation for the discretization of
the Darcy flux, we can mention the Mixed
Finite Element (MFE) method [14], theMimmetic Finite Difference (MFD) [19], theDiscontinuous Galerkin (DG) method [27]and the Multipoint Flux Approximation(MPFA) finite volume schemes [1; 10]. In
this context, we should refer to a class ofrecently developed non-orthodox MPFA
schemes that have "full pressure support",such as the: MPFA-D (Diamond Stencil)[7; 11], extended for 3-D by Lira et al., 2021
[20], the MPFA-H (Harmonic Points) [2] andthe MPFA-QL (Quasi-Local Stencil) [26]which are quite robust to deal with strong
physical and mesh anisotropy and are able
to accurately reproduce piecewise linear
solutions through a linear preserving
interpolation with explicit weightsthat avoids solving locallydefined systems of equations

that are typical of more classical(a)
MPFA methods such as the
MPFA-O and the MPFA-FPS[1; 10]. Differences among theselinear schemes range from theimposition of local continuity
throughout the control surfaces,
which is particularly important in
the case of heterogeneous and,possibly, discontinuous permeability tensors, to the definition of
the stencil used in the flux
approximation. In Figure 1, we present
the pressure field obtained with the MPFA-D
formulation in a 3-D simulation, which
reproduces a piecewise linear solution fora heterogeneous, and discontinuous fullpermeability tensor.
It is well-known that no linear formulation,
including the most robust GFEM, MFEM,
MFD, DG and MPFA methods can guarantee satisfaction of the Discrete Maximum
Principle (DMP) or at least monotone solutions for general non-orthogonal meshesor arbitrary anisotropic full permeability
tensors [17] for the diffusion operator.
For certain pathological cases, this fact
can generate non-physical Darcy fluxes
associated to spurious pressure oscillations
and fictitious gas as a consequence of thepressure field falling below the bubble point
in different points of the reservoir. On theother hand, nonlinear formulations can bebuilt to satisfy the DMP or, at least, somemonotonicity requirements. In this context,
several nonlinear finite volume (NLFV)
methods have been developed in the lasttwo decades [8; 12; 17; 25; 32]. Usually,in these schemes, the final nonlinear
algebraic system of equations is solved by
the first-order Picard iteration method to(a)Problem definition(b)

Figure 1:
The Oblique Drain test:(a) pressure distribution; (b) isosurface contours.
Mesh size: 46,923 volumes [20].
guarantee monotone solutions [25]. In
Figure 2, we can see the solution obtainedby a NLFV that satisfies the DMP.
Convergence history is also shown fordifferent 2D meshes, with and without
Anderson Acceleration (AA.
High-Order and Spectral Methods for theTransport Equation
In petroleum reservoir simulation, thestandard method to discretize the advective
terms in the transport equations is theclassical First Order Upwind (FOU) method.
Even though it is robust and simple toimplement, it is well known that it suffersfrom strong artificial diffusion and Grid
Orientations Effects (GOE). This latterproblem, in which completely different andfalse solutions can be obtained by using
meshes with different spatial orientations,and which is dependent of the relative
direction between the multiphase fluid flow(b) Pressure field(c) Convergence history

Figure 2:
NFVM solution using Picard iteration with convergence acceleration of a single-phase flow in aheterogeneous and highly anisotropic porous media [8]:(a) Anisotropic permeability tensor; (b) Pressure solution; (c) Picard convergence history.
3 iacm expressions 50/22

and the grid lines, can be extremely
important, particularly for many real-world
applications involving flows where themobility of the resident fluid is much smaller
than the mobility of the displacing fluid. In
these cases, the so called Saffman-Taylor
front instability may occur, causing acompetition between non-physical viscous
fingers and the diffusive effects that try todissipate sharp fronts. These problems
include miscible displacements, therecovery of heavy oils, and other Enhanced
Oil Recovery Techniques (EOR), such as
polymer injection, thermal recovery or thealternate injection of water and gas (Water
Alternate Gas – WAG), etc. [16; 26; 29; 31].

“ Our strategy
has proven toaccurately
handle oilwater fluid
displacements
withheterogeneousandanisotropic fullpermeability
tensors formatrix andfractures innaturally
fractured
reservoirs.”

In this context, the development of highorder, truly multidimensional and locallyconservative formulations that can be usedin general unstructured meshes is of utmost
importance to reduce artificial numerical
diffusion and GOE, and to improve thereliability of the approximate numericalsolutions. Second order upwinding iscommonly available in petroleum reservoir
simulators, and 2nd order MUSCL(Monotonic Upstream-Centered Scheme
for Conservation Laws) type formulations
for two and three phase flow formulations[5; 22]. Usually, in these cases, thepressure-velocity problem is solved by theControl Volume Finite Element (CVFEM)
formulation, MFEM (Mixed Finite Element),
MPFA or NLFV methods [10; 13; 14].
However, in the context of fluid flow inheterogeneous petroleum reservoir oraquifers, higher than second order is yet to
be more explored, even though some efforts
can be found using k-exact formulations[35]; ENO and WENO approaches [18] andDG methods [27]. More recently, very highorder (VHO) spectral formulations havebeen developed for 1D and 2D oil-water displacements, including the Spectral Volume(SV), Spectral Difference (SD), nodal DG
and the Flux Reconstruction/Correction

Procedure via Reconstruction (FR/CPR)
methods [26]. These methods have shownhigh potential to incorporate h-p typeadaptivity and handle extremely complex
compositional flows in naturally fractured,heterogeneous and anisotropic porous
media. In [26] we have used the MPFA-QL
coupled with the FR formulation to simulate
oil-water displacements in highly heterogeneous and anisotropic oil reservoirs withexcellent results. In order to properly couple
cell centred MPFA methods with the nodal
spectral formulations, it is necessary toobtain an appropriate velocity reconstruction
within the primal control volumes (CVs)of the computational mesh. This can beachieved by a reconstruction operator basedin the mixed finite element basis functionsof Raviart-Thomas (RT) or Brezzi-DouglasMarini (BDM) [4]. This operator receives
the input fluxes through each control
surface and returns the point-wise valuesof the velocity field everywhere within
the CV, where the saturation must
be computed. In fact, a key aspect of
VHO methods used for the numericalsimulation of petroleum reservoirs is their
low computational cost with a similar
degree of accuracy when compared totheir lower order counterparts.
An example retrieved from Moshiri andManzari (2019) [22] and that consists of amulticomponent fluid flow, in which CO_2 isinjected in a 1D reservoir containing C1, C4,
C10 and C16 with the same molar fractions
is shown in Figure 3. For this case, it isassumed that there is no volume change on
mixing, as the components change phases
and constant K-values are used. First order
upwind (FOU), second order MUSCL andhigher order Flux Reconstruction solutions
are shown considering different levels ofmesh refinement (i.e., 128, 256 and 512
Control Volumes - CVs), highlighting theimproving in accuracy by both h and p meshrefinement.

(a) 128 CVs(b) 256 CVs(c) 512 CVs
Figure 3:
Grid convergence study of overall global composition of C1 throughout the domain using different levels of meshrefinement (i.e., 128, 256 and 512 Control Volumes – CVs).
iacm expressions 50/22 4

To further reduce GOE, truly multidimensional formulations have also been used inliterature. First and second order mass
weighted schemes have been originallydevised for the Navier-Stokes equations in[30]. In the context of petroleum reservoir
simulation, we should mention the worksof Hurtado et al., 2007 [13] and Kozdon etal., 2011 [16]. More recently, explicit andimplicit truly multidimensional upwinding
has been proposed in the works of Souzaet al., 2018 [35] and Keilegavlen et al., 2012[16]. In [35], authors have combined a
MUSCL-type method with the Multi-dimensional Limiting Process (MLP) [24]. This
formulation has shown improved monotonicity properties and the ability to produce
reasonable physical solutions with reduced
GOE and artificial numerical dissipation,
considering fluid flows with high mobility
ratios and very distorted 2D unstructured
meshes as it can be seen in Figure 4.
Multiscale-Multilevel Finite Volume
Methods
With advances in geological characterisation, petrophysical models of subsurface
petroleum reservoirs have been refined to ascale of orders of magnitude higher thanstandard petroleum reservoir simulators.
The challenges associated with simulating
unconventional reservoirs, such as thosewith highly heterogeneous and anisotropic
permeability fields and their convoluted
geometry, lead to simulations with everincreasing computational power. As aresult, multiple simulations on the highresolution geological scale are not feasible.
The practical and most common solution
to this problem is to use an upscaling technique that homogenises the petrophysicalproperties and thus transfers the informationfrom the high-resolution geological andstatic grid to the lower-resolution simulation

Figure 4:
Water saturation front obtained for a distorted mesh with
64 x 64 CVs, using standard first and second order upwind
schemes and different multidimensional flow-oriented schemes
and the reference solution using 37,996 CVs(see [35] for further details).
grid. The main disadvantage of this technique is that the high-resolution aspectsof the flow are lost in this process, as thegeologic information remains on the highresolution grid. A promising solution to deal
with the computational limitations and takeadvantage of the high-resolution scale is theuse Multiscale methodologies [14; 15; 38].
The idea behind these schemes is to create
a set of operators by which the system of
equations of the high-resolution grid can beprojected onto the space of the low-resolution grid, where it can be solved and projected back onto the high-resolution grid. In
this way, it is possible to obtain a reasonably
accurate approximate solution [33] at thegeological scale. In the context ofstructured meshes, an interest approachthat incorporates the concept of automaticmesh adaptation in the framework of the
MsFV is the Algebraic Dynamic Multilevel(ADM) of Cusini et al., 2016 [9]. The ADM
uses the MsFV multiscale operatorsin the simulation while maintainingsome high-resolution volumes at the finescale to improve the accuracyof the solution in critical regions of thedomain, including the wellbore, channels
or faults, and regions where importantvariables change dynamically with time,such as the saturation or molar concentration fronts. Usually, the ADM method and its
variants have in common that a uniform
level is used in each coarse volume,
implying that all volumes belonging toa coarse volume at any level must be at thecoarse or fine scale. To circumvent thislimitation, recently, we have developed the
Nun-Uniform Algebraic Dynamic Multiscalesolver (NU-ADM) [29], that can handle
5 iacm expressions 50/22

(a) Classical ADM-AMS
ǁepǁ2 =18.7% ǁepǁ∞ =340%(b) fine-scale(c) ADM with A-AMS
ǁepǁ2 =5.6% ǁepǁ∞ =8%

Figure 5:
Pressure field for the layer 85 of the 10th CSP-SPE, obtained by employing: (a) the standard ADM using the basis
functions from AMS; (b) fine-scale (reference) solution; (c) A-AMS with hlim = 1, over the corresponding dual
agglomerated mesh [29].
culation of the prolongation operator using
non-uniform levels at each coarse-scale
agglomerated dual coarse volumes chosen
control volume. Furthermore, a new extenadaptively (A-AMS, [29]) to mitigate thesion of the AMS (Algebraic Multiscalespurious oscillations. Figure 5 presents
Solver) [37] that mitigates the unphysicalthe improvement of the pressure solution
oscillations that occur when the decouplingby using A-AMS against the standard AMS,
of basis functions takes place in certain
considering the 85th layer of the 10th SPE
regions of the simulation. In these cases,
Comparative Solution Project [29].
by examining the coarse-scale matrix, ourmethod detects and performs a local recalEven though MsFV methods have thrived byusing structured meshes and linear TPFA
discretizations, until recently, very few
strategies have been designed to makeuse of unstructured meshes. In this context,
our research group has investigated anddeveloped methods to extend the MsFV(Multiscale Finite Volume) family to allow
simulations on general unstructured grids at
all scales of the simulation. The Multiscale
Control-Volume (MsCV) framework was thefirst scheme to allow consistent simulation
with true unstructured grids. This wasachieved by replacing the classical linear
Two-Point Flux Approximation (TPFA),
which is only consistent on k-orthogonal
grids, by the MPFA-D on the Multiscale
Restriction Smoothed Basis (MsRSB) [23]procedure. For the set of attempted
problems in Souza et al., 2020 [33], theMsCV was capable to reproduce the finescale solutions using truly unstructured grids
in both scales maintaining the most important aspects of physics and with acceptable(b)(a)
losses of accuracy.
Figure 6:
Two-phase flow simulation for the layer 85 of the 10th CSP-SPE:
Another breakthrough was the development
and saturation fields obtained employing:of the Algebraic Multiscale Solver for(a) fine-scale (reference) solution; (b) Multiscale NU-ADM [29].
Unstructured grids (AMS-U) [34] as a
iacm expressions 50/22 6

natural extension of the AMS [37] method.
The core aspects of the AMS-U include a
new strategy to compute the multiscale
geometric entities, i.e., primal and dualcoarse grids, the modifications to theprolongation operator to avoid the leakageof the basis function and the use of the
MPFA-D method for the fine-scale flux
approximation. In Figure 7 we show
water saturation contours at t=0.545 PVI
for a problem involving a highly heterogenous and reservoir with two injection wells
and one producer.
Modelling Fluid Flow in Naturally
Fractured Reservoirs
Naturally fractured reservoirs have received
increasing attention from the oil industry
as recent estimates indicate that most of
the remaining exploitable oil and gas fields
are of this type [3]. Fault development,
folding, diagenetic processes and sedimentsubsidence can cause natural rock fractures
over geological time, ranging in size froma few micrometres to several kilometres.
Modelling multiphase and multicomponent
fluid flow in these media is challenging
because, in addition to the presence of
fractures, there is the natural complexityof the depositional environments and the
difficulty of creating appropriate meshes
for such cases. In this context, it is fundamental to develop robust and accurate
numerical methods capable of dealing withall these challenges and to find suitablemodels that take into account the influenceof fractures in different scales. Classicalapproaches to handle fractured reservoirs
include the use dual porosity and dual
permeability models or the use of simple
transmissibility multipliers. These approaches are limited, especially when it comesto the simulation of multiphase andmulticomponent flows [3]. A more robust
and accurate approach involves the use ofDiscrete Fracture Models (DFM).
In DFM, fractures are explicitly represented
at the computational mesh and additional
degrees of freedom are associated to these
entities [28]. Among the different DFM,
we can mention the equidimensional,
the lower dimensional (LDFM) and the
hybrid grid (HyG) models [6; 28]. In theequidimensional approach, fractures are
represented in the geometric mesh with
its real aperture by using the meshelements to represent fractures
and the rock matrix. In the LDFMs, flow
equations for fractures are discretizedseparately from the matrix, in a (n−1)
dimensional subdomain. On the other
hand, in the HyG approach fractures are
expanded to n-dimensional objects in the

(a)

(b)

(c)

Figure 7.
Two-phase flow of oil and water in a reservoir with a random
permeability field: (a) Representation of the log of the permeability
field (injection wells in red dots and production well in white dot);(b) Fine scale saturation field; (c) AMS-U multiscale saturation field.
Both at t = 0.545 PVI [34].
computational domain, and the equations
for fractures and the rock matrix can bediscretized together, taking advantage
from full pressure support MPFA methods
to solve the pressure equation. Therefore,
the HyG seems to present a goodcompromise between accuracy andcomputational costs, due its capability toproperly represent complex geometries
and anisotropy within fractures, without
the computational overhead associatedto the explicit discretization of fracturesof the equidimensional approach.
Recently, we have developed the HyG
method in the context of the MPFA-D
method to solve the pressure equationand the classical FOU (First Order Upwind)
method using a Sequential implicit method(SEQ) [6] and the streamline method [36]to solve the saturation equation with an
MIMPES (Modified Implicit Pressure
Explicit Saturation) methodology. Our
strategy has proven to accurately handle
oil-water fluid displacements with
7 iacm expressions 50/22

similar cases, the SLS-HyG method is
one order of magnitude faster than the
SEQ scheme.

(a)Computational configurationb) Streamlines (PVI – 0.5)(c) FOU-HyG PVI - 0.5(d) SLS-HyG PVI - 0.5
Figure 8:
The ¼ of five-spot two-phase flow configuration with multiples
connected fractures: (a) Unstructured quadrilateral mesh;(b) Streamlines and time-of-flight field results;(c) Saturation field using SIM with FOU-HyG scheme;(d) Saturation field using MIMPES with SLS-HyG PVI scheme [36].

heterogeneous and anisotropic fullpermeability tensors for matrix andfractures in naturally fractured reservoirs.
In Figure 8 we consider a ¼ of five-spot
configuration in a reservoir with multiple
non-aligned and arbitrarily distributed
connected fractures. For this and otherConcluding Remarks
To conclude, we would like to list several
other topics of strong interest for those
who are planning to get involved with thenumerical modelling of fluid flow inheterogeneous and anisotropic porous
media, such as: Multiscale-Multiphysics,
Sokes-Brinkman (Karst Reservoirs),
Geomechanics, Coupling of thermalhydro-mechanical, Complex setups,
EOR (Enhanced Oil Recovery),IOR (Improved Oil Recovery), CO_2
sequestration, Non-conventional gas:tight gas, shale gas, CBM (Coal Bed
Methane), Incorporation of all ingredientsof HPC, Workflows and Computing,Hybrid models (phenomenological +data driven).
Acknowledgments
The Authors would like to thank allcolleagues, students and former students,of the PADMEC research group involved
in reservoir modelling and simulation,in particular, to those who provided theresults presented in this paper:
Prof. Fernando R. L. Contreras;
Prof. Márcio R. A. Souza;
Dr. Gustavo R. Galindez;
MSc. Artur C. R. de Souza;
MSc. José Cícero A. dos Santos;
MSc. Túlio M. Cavalcante,
MSc. Ricardo J. M. Lira Filhoand
BSc. Maria Eduarda S. Galindo.
The authors also gratefully acknowledge
the financial support provided by theBrazilian Research Councils: CAPES,
CNPq, FACEPE and by the Energi
Simulation foundation. l

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https://doi.org/10.2118/106254-PA
9 iacm expressions 50/22

D eep R einforcement L earning for
O ptimal F low C ontrol
E. Hachem*
J. Viquerat
A. Larcher
P. Meliga
MINES Paristech,
CEMEF
PSL Research University*elie.hachem@minesparis.psl.eu

1Introduction
During the past decade, machine learning
methods, and more specifically deep neuralnetwork (DNN), have achieved great successes in a wide variety of domains.
State-of-the-art neural networkarchitectures have reached astonishing
performance levels in image classification
tasks [1, 2], speech recognition [3] orgenerative tasks [4]. With a generalized
access to GPU computational resources
through cheaper hardware or cloudcomputing, such advances have been
paving the way for a general evolutionof the reference methods in these
domains, both at the academic andindustrial levels.
The quick rise of neural networks hasgone along with important progress in thedomain of decisionmaking techniques, by
the coupling of DNNs with reinforcement
learning algorithms (called deep reinforcement learning, or DRL). Several major
obstacles that had been hindering classicalreinforcement learning have been liftedusing the feature extraction capabilities
of DNNs, and their ability to handle
high-dimensional state spaces.
Unprecedented efficiency has been

Figure 1:
RL agent and its interactions with the environment

achieved in many domains such as
robotics [5], language processing [6],or games [7, 8], but DRL has also provenuseful in many industrial applications, suchas autonomous cars [9, 10], or data centercooling [11].
The efforts for applying DRL to fluid
mechanics are ongoing but still at an
early stage, with only a handful ofpioneering studies providing insight into
the performance improvements to bedelivered in the field [12]. Nonetheless,from a few liminal contributions in 2016[13] and early 2018 [14], the domain
has undergone an increasing inflow ofcontributions, that pinnacled in 2020,
with no less than 16 preprints and articles,
most of them focusing on academic dragreduction problems. This enthusiasm
can be explained by two main factors:
first, the sustained commitment from themachine learning community, that has
allowed concurrently expanding thescope from computationally inexpensive,
low-dimensional reductions of theunderlying fluid dynamics to complex
Navier–Stokes systems, all the way toexperimental set-ups. Second, theincreasing number of open-source
initiatives [15, 16, 17], that has led to an
accelerated diffusion of the methods inthe community.
2
Deep reinforcement learning
Reinforcement learning (RL) is a classof methods designed for decision-making
problems, in which an agent learns tointeract with an environment by(i) observing it,(ii) taking actions based on these figure 1.
In practice, the reward embodies theobjective of the control law, and must be
designed on a case-by-case basis for ahigh value (resp. a low value) to reflect
a good choice (resp. a bad choice)of action given the last provided
observation. In any given state, the agent
seeks the adequate action maximizing its
discounted cumulative reward over an
episode, defined as:1)
iacm expressions 50/22 10where rt is the reward received for taking
action at given state st, T is the horizon of
the trajectory (i.e. the terminal time station),and g ∈ [0, 1] is a discount factor thatweights the relative importance of present
and future rewards. Such a problem
can be formulated as a Markov Decision
Process, for which a variety of methods
have been developed, that involve
estimating either a value function (valuebased methods), a policy, i.e. a probability
distribution over actions given states(policy-based methods), or a mixture ofboth (actor-critic methods)2.
In a deep reinforcement learning context,
the agent is a deep neural network
patterned after the neural circuits formedby neurons in human brains. The mostgeneral form of neural network architectureis the fully connected DNN, in which theprocessing units (the artificial neurons)
are stacked in layers and information
propagates forward from the input layer tothe output layer via “hidden” layers. Eachneuron performs a weighted sum of its
inputs to assign significance with regard to
the task the algorithm is trying to learn,adds a bias to figure out the part of theoutput independent of the input, and feeds
an activation function that determines
whether and to what extent the computed
value should affect the outcome. The
neural network learns to represent therelation between input (action) and output(reward) data by repeatedly adjusting theweights and biases by back-propagation,from the output layer back through thehidden layers to the input layer (aprocess known as training). This is usually
done using stochastic gradient method tominimize the value of a well-chosen loss
function (representing the quality of the
network prediction), most always based on
an expected cumulated reward computed
using state-action-rewards samples
obtained from interacting with theenvironment.
3

Deep reinforcement learning andflow systems
Regarding the DRL-based control ofcomputational fluid dynamics (CFD)
systems, most efforts in the community
have so far touted the high potential of
the approach on textbook problems,prominently in the context of low Reynolds
number drag reduction. Nonetheless,considerable efforts are needed to(i) consolidate the acquired knowledge,(ii) further shape the capabilities of themethod, (iii) bridge the gap between
DRL and advanced numerical methods for

multiscale, multiphysics CFD systems,
Two key ingredients are mandatory for thispurpose: an efficient DRL algorithm forlearning, and a scalable CFD solver toaccurately predict the computationallyexpensive numerical solutions from which
to extract the reward fed to the DRL agent.
The choice of the latter can dramatically
impact on the training time required toreach a well-trained control agent, as
performing a relevant network update
requires evaluating a sufficient numberof actions drawn from the current policy,
which in turn requires computing thesame amount of rewards from resourceexpensive numerical simulations (this canamount to several tens of CPU-hours per
reward evaluation).
The Computing and Fluid Research(CFL) group at the Centre for Material
Forming processes (CEMEF) research
center has gone into DRL-CFD as part of
the MINDS project (Mines Initiative for
Numerics and Data Science), developed at
MINES ParisTech (Paris School of Mines)
with the support of the French National
Research Agency. MINDS is interested
in the convergence of high performancecomputing and data science, and thus
aims at developing a joint digital researchand development platform bringing together
advances in both fields. The long-term
ambition is to revisit the current designof control strategies for computationallyexpensive flow problems representativeof industrial applications (high Reynolds
numbers, complex geometries, multiple
phases and/or non-Newtonian fluids),
which requires flexible tools that can beeasily integrated in tuning-free, ready-touse, near-real time optimization procedures.
We review below pending problems invarious scopes (e.g., optimization of steadyand unsteady aerodynamics coefficients,
control of industrial thermic processes,
shape optimization) tackled in this context,
for which several DRL algorithms havebeen coupled to CimLib- CFD, an in-house,massively parallel, multiphysics C++ finite
element library. This has been done ina high-performance computing context,
to allow distributing actions to multiple
environments running in parallel, eachof which executes a self-contained MPIparallel CFD simulation and feeds data
to the DRL algorithm (hence, two levels ofparallelism related to the environment andthe computing architecture).

2

We restrict here on model-free methods and voluntarilty omit model-based methods, inwhich the agent interacts with a model of the environment, not the environment itself.

11 iacm expressions 50/22

3.1 Drag reduction
Drag reduction is a topic of tremendous
societal and economical importance, as
reducing the overall drag by just a few
percent while maintaining lift can help
reduce fossil fuel consumption and CO2
emissions while saving several billion
dollars annually in applications such as
ocean shipping or airline traffic. In [18],several open-loop drag reduction problems
modeled after the unsteady Reynolds
averaged Navier–Stokes (uRANS)
equations are tackled to showcase therelevance of DRL-CFD for the control of
turbulent cylinder flows (with Reynolds
numbers ranging from a few thousands
up to a few ten thousands, based on thecylinder diameter and the free stream
velocity). The DRL algorithm is singlestep PPO, a tweaked version of the PPO
algorithm (Proximal policy optimization [19],that has quickly gained prominence in the
DRL community and is by far the mostcommon algorithm exploited in the context
of DRL-based control for fluid mechanics),
intended for situations where the optimal
policy to be learnt by a neural network does
not depend on state. Such an approachproves relevant to map the best positions
for placement of a small control cylinder in
the attempt to reduce the drag of a squarecylinder (see figure 2(a)), the achieved
reduction by 30% being in good agreementwith reference experimental data available
from literature. The method also successfully reduces the drag of the fluidic pinball,an equilateral triangle arrangement ofrotating cylinders immersed in a turbulent
stream. Consistently with referencemachine learning results from the literature,drag is reduced by almost 60% using aso-called boat tailing actuation made up
of a slowly rotating front cylinder and two
downstream cylinders rotating in opposite
directions so as to reduce the gap flow in
between them; see figure 2(b).
3.2 Thermal control
Thermal control, defined as the ability
to finesse the thermal properties of a
volume of fluid (and of the solid objects
inside) into a certain desired state, is anatural field to extend the scope of the
DRL methodology and to increase thecomplexity of the targeted applications.
So far, it has drawn little attention from thecommunity despite its social significance.
For instance, heat/cool exchangers areused in a broad range of industrial applications to regulate process temperaturesby heat or cool transfer between fluid
media, which in turn ensures thatmachinery, chemicals, water, gas, and othersubstances remain within safe operating
conditions. Green building engineeringand aircraft cabin climatization are otherFigure 2:
Drag reduction in the context of turbulent cylinder flows.
(a) Control of the square cylinder flow: the position of a small, circular control cylinder around a main,square cylinder is evolved by DRL in order to minimize the mean drag of the two-cylinder system.
The positions investigated by DRL are shown in as the grey circles, with the light red circles showing
high reward positions spanned over the course of optimization, and the dark red circles showing
those high reward positions spanned over the last 5 episodes.
The green triangle is the optimal position determined experimentally at a similar Reynolds number [20].
(b) Control of the fluidic pinball: DRL is used to evolve three cylinders individual angular velocities in order tominimize the mean drag of the three-cylinder system.
For this case, the reward comprises two terms, a first term equal to the power of drag andassociated to performance, and a second term estimating the power to be supplied
to the rotating cylinders and thus associated to cost.

(a)iacm expressions 50/22 12

(b)

(a)

(b)

Figure 3:
Forced convection in a rectangular cavity the context of workpiece cooling.
(a) The top cavity side is flush with 3 identical holes whose distribution is optimized
by DRL in order to minimize the local temperature gradients of a 3-D workpiece.
(b) The position of a 2-D workpiece relative to two fixed injectors is optimized for thesame purpose, with the optimal position shown at the bottom
fields focusing on regulating indoor thermal
conditions (temperature, humidity).undersubstantial variations of the ambientconditions to provide high-quality comfort
environments. In many manufacturing
processes, thermal conditioning is also
intended to improve the final mechanical(e.g., hardness, toughness, resistance),
electrical, or optical properties of the
product, the general picture being thathigh temperature gradients are useful to
speed up the process but generally harm
the quality of the outcome because of heattransfer inhomogeneities caused by theincreased convection by the fluid particles.
In [21], the scope of single-step PPO is
extended to the control of conjugate heattransfer systems governed by the coupled
Navier–Stokes and heat equations. The
approach is found capable of improving
the homogeneity of temperature acrossthe surface of two and threedimensional
hot workpieces under impingement cooling.

Several control strategies are assessed,
for which the position of multiple cold air
injectors is optimized relative to a fixed
workpiece position, whose increasing
design complexity translates into lessconstrained operation when it comes tooptimizing a practically meaningful device.
For instance, a so-called fixed domain
decomposition strategy in which each
injector is forced to sit in a different
subdomain yields a heavily constrained
optimization problem, relevant forcases where the design is rigid and thepractitioner has limited freedom to act.
Conversely, a so-called free strategy, inwhich all injectors are independent andfree to move along the top cavity wall,
yields a mildly constrained optimization
problem, relevant for cases where thedesign is flexible and the practitioner
has great freedom to act. The flexibilityof the CFD numerical framework also
allows optimizing the workpiece position
13 iacm expressions 50/22

relative to a fixed, symmetrical actuation
injector distribution. The optimal position
turns to be offset from the symmetry axis,
which had not been anticipated, andstresses the ability of the DRL-CFD
framework to effectively explore anddiscover non-trivial solutions fromunforeseen parameter combinations.
3.3 Wing shape optimization
Shape optimization is ubiquitous inengineering applications ranging frommagnetostatics, image restoration andsegmentation, to nano-optics, just to
name a few. Shape optimization in fluid
mechanics dates back to the pioneering
work of Pironneau on the minimization ofenergy loss in Stokes and Navier–Stokes
flows [6, 7]. Since then, it has become anincreasingly important research topic in
the attempt to enhance drag reduction
capabilities, which is due to the evergrowing concerns on aerodynamic energy
efficiency. Shape optimization generally
consists in figuring out a fixed shapemeeting a set of required criteria (e.g. highFigure 4:
Shape optimization in the context of optimal wing design.
DRL is used to maximizethe lift-to-drag ratio under
the constraint of a fixed section area using(a) 2-D and (b) 3-D environments
(a)

(b)

iacm expressions 50/22 14

lift-to-drag ratio, low pressure loss) andtherefore requires optimizing stateindependent parameters, which is per
se the original purpose of single-step
DRL. In [22], Policy based optimization(PBO [23]), an in-house algorithm
developed by the CFL group under thepremise that convergence to the optimal
can be made faster if actions are sampled
anisotropically from full covariancematrices, is applied to aerodynamic shape
optimization problems. Two-dimensional
airfoil sections with fixed camber line,
chord direction and angle of attack are
generated by varying a chord length anda thickness distribution. The uppe andlower sides are discretized into a small
number of control points equally spaced in
the camber line direction. All shapes areclosed and symmetrical with respect tothe chord line, as achieved forcing zerothickness at the edges and identical(half)-thicknesses at each forward andrearward facing points. Following theseminal work of [17], consecutive points
are connected by a cubic Bézier curveusing local position and curvature
information. The final step consists ofsampling all Bézier curves and in exportinga closed loop, to be either used as animmersed mesh in a 2-D environment,
or extruded in the off-body direction toserve as an immersed mesh in a 3-D
environment. At zero incidence, theproposed framework successfullyreduces the drag of the equivalent cylinder(i.e., the cylinder of same cross-sectionalarea) by 48% at a Reynolds numbers in
the range of a few hundreds. At an
incidence of 30, it increases the lift todrag ratio of the equivalent ellipse by
13% in 2-D and 5% in 3-D at a chord
Reynolds numbers in the range of a few
thousands (figure 4). Although the low
number of degrees of freedom inevitably
constrains the range of attainable shapes,
the optimal is systematically found to
perform just as well as a conventional
airfoil, despite DRL starting from theground up and having no priori knowledgeof aerodynamic concepts. l“ Although the low number of degrees
of freedom inevitably constrains
the range of attainable shapes,
the optimal is systematically foundto perform just as well asa conventional airfoil,
despite DRL starting from theground up and having no priori knowledge of
aerodynamic concepts.”

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[4] J. Gui, Z. Sun, Y. Wen, D. Tao, and J. Ye. A review on generative adversarial networks: algorithms, theory, andapplications. arXiv preprint arXiv:2001.06937, 2020.
[5] L. Pinto, M. Andrychowicz, P. Welinder, W. Zaremba, and P. Abbeel. Asymmetric actor critic for image-based robot
learning. arXiv preprint arXiv:1710.06542, 2017.
[6] D. Bahdanau, P. Brakel, K. Xu, A. Goyal, R. Lowe, J. Pineau, A. Courville, and Y. Bengio. An actor-critic algorithm forsequence prediction. arXiv preprint arXiv:1607.07086, 2016.
[7] V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller. Playing Atari with deep
reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.
[8] D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen,
T. Lillicrap, F. Hui, L. Sifre, G. van den Driessche, T. Graepel, and D. Hassabis. Mastering the game of Go without humanknowledge. Nature, 550, 2017. Deep reinforcement learning for optimal flow control A Preprint[9] A. Kendall, J. Hawke, D. Janz, P. Mazur, D. Reda, J.-M. Allen, V.-D. Lam, A. Bewley, and A. Shah. Learning to drive in aday. arXiv preprint arXiv:1807.00412, 2018.
[10] A. Bewley, J. Rigley, Y. Liu, J. Hawke, R. Shen, V.-D. Lam, and A. Kendall. Learning to drive from simulation without realworld labels. arXiv preprint arXiv:1812.03823, 2018.
[11] W. Knight. Google just gave control over data center cooling to an AI. http://www.technologyreview.com/s/611902/
google-just-gave-control-over-data-center-cooling-to-an-ai/, 2018.
[12] P. Garnier, J. Viquerat, J. Rabault, A. Larcher, A. Kuhnle, and E. Hachem. A review on deep reinforcement learning for
fluid mechanics. Computers & Fluids, 225:104973, 2021.
[13] G. Novati, S. Verma, D. Alexeev, D. Rossinelli, W. M. van Rees, and P. Koumoutsakos. Synchronisation through learning
for two self-propelled swimmers. Bioinspiration & Biomimetics, 12(3):036001, 2017.
[14] S. Verma, G. Novati, and P. Koumoutsakos. Efficient collective swimming by harnessing vortices through deep
reinforcement learning. Proceedings of the National Academy of Sciences, 115(23):5849–5854, 2018.
[15] J. Rabault, M. Kuchta, A. Jensen, U. R´eglade, and N. Cerardi. Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control. Journal of Fluid Mechanics, 865:281–302, 2019.
[16] V. Belus, J. Rabault, J. Viquerat, Z. Che, E. Hachem, and U. Reglade. Exploiting locality and translational invariance todesign effective deep reinforcement learning control of the 1-dimensional unstable falling liquid film.
AIP Advances, 9(12):125014, 2019.
[17] J. Viquerat, J. Rabault, A. Kuhnle, H. Ghraieb, A. Larcher, and E. Hachem. Direct shape optimization through deep
reinforcement learning. Journal of Computational Physics, 428:110080, 2021.
[18] H. Ghraieb, J. Viquerat, A. Larcher, P. Meliga, and E. Hachem. Single-step deep reinforcement learning for open-loop
control of laminar and turbulent flows. Physical Review Fluids, 6:053902, 2021, in press.
[19] J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov. Proximal policy optimization algorithms.
arXiv preprint arXiv:1707.06347, 2017.
[20] T. Igarashi. Drag reduction of a square prism by flow control using a small rod. Journal of Wind Engineering andIndustrial Aerodynamics, 69–71:141–153, 1997.
[21] E. Hachem, H. Ghraieb, J. Viquerat, A. Larcher, and P. Meliga. Deep reinforcement learning for the control ofconjugate heat transfer. Journal of Computational Physics, 436:110317, 2021.
[22] H. Ghraieb, J. Viquerat, A. Larchet, P. Meliga, and E. Hachem. Single-step deep reinforcement learning for two- andthree-dimensional optimal shape design. Physics of Fluids (submitted), 2022.
[23] J. Viquerat, R. Duvigneau, P. Meliga, A. Kuhnle, and E. Hachem. Policy-based optimization: single-step policy gradientmethod seen as an evolution strategy. arXiv preprint arXiv:2104.06175, 2021.
15 iacm expressions 50/22

A Primer onMathematical Modelling
Alfio Quarteroni & Paola Gervasio
Springer, Cham, Switzerland, 2020

B
O
O
K

ISBN:
978-3-030-44540-9, 238 pages, soft cover, €20 (List Price).
Contents: Preface; About the Book; Main Symbols; 1: A Warm-Up to Models;
2: Getting Started; 3: Reckoning with the Computer; 4: A virtual Surfer for the Web;
5: A network of Capillaries; 6: Predators and Preys in the Maths Ocean;
7: Take-Home Message; 8: Solutions to Exercises; Index.
So far, this Book Review section has always reviewed books written for readers who aremembers of the Computational Mechanics (CM) community. This book (translated from
Italian by the applied mathematician S.G. Chiossi), is an exception, as it is apparently
intended for another type of audience, such as talented high-school students, studentsof the non-exact sciences (such as humanities and social sciences) who venture into
the territory of modeling, and in general – curious people who are interested to knowwhat modeling is about, and perhaps wish to start practicing it in their area of interest.
As a consequence, the book requires no background in calculus or linear algebra.
A reader who has not heard of a derivative or a vector can still read this book andunderstand how modeling is done, through careful explanations and several importantscientific examples. This is an impressive educational achievement.

R
E
V
I
E
W

The book is extremely well written and is spectacular in its beauty. It is full with colorful
pictures and figures, with many clear graphs, algorithms, codes (written in Octave) andtables, which makes reading the book a real joy. The topics it covers include numericalintegration (only the most basic integration rule is discussed), Gauss elimination for thesolution of the system Ax=b (chap. 2), round-off errors (chap. 3), fixed-point iteration and
the Google algorithm for ordering sites (chap. 4), modeling the blood system (chap. 5;
see figure below); finite difference calculation of the derivative, and the Euler method forsolving an ODE and a system of ODEs (chap.
This is a superb book for readers doing their initial steps in the wonderful world ofmodeling. I recommended it to my 17-year-old nephew, who is now reading
it with great enthusiasm. lfrom the heartartery capillaries

3

C 23

vein

1
2

5

6

4

arterioleby

Dan Givoli
Technion Israel Institute ofTechnology
givolid@technion.ac.il
iacm expressions 50/22 16

towards the heart

venuleModeling the blood system.
Left: a capillary bed (Fig. 5.2 of the book).
Right: graph representation(part of Fig. 5.12 of the book)for all inclusions under KSCM please contact
KSCM office
Kscm.society@gmail.com

KSCM 2021
Winter Workshop
SCM 2021 Winter Workshop was held in the Seoul Lotte World Tower, Seoul,
Korea, on 17 December 2021. The workshop organized by the Korean Society
for Computational Mechanics aims to bring together Korean researchers in the field ofcomputational mechanics and to encourage research exchange and networking.
Thirty seven professors gathered and listened to research on autonomous driving andcomputational mechanics of cars, UAMs and marine vehicles.

K

Three speakers – Kichun Jo (Konkuk
University), Seungkeun Kim(Chungnam National University)
and Jinwhan Kim (KAIST) – gave
lectures on the recent technology
in the autonomous drive of vehicles
related with the computational
mechanics.
Further information will be found at:
http://www.kscm-society.org/. l

Figure 1:
A speaker gave a presentation
Figure 2:
The former
KSCM president,
Prof. Chae gave acongratulatory speech

Figure 3:
The commemorative photoof participants

17 iacm expressions 50/22

Gruppo Italiano di
Meccanica Computazionale

MINISYMPOSIUM GIMC
(Italian Group of Computational Mechanics)
at the AIMETA Congress,
Palermo, September 4-9, 2022

he AIMETA Computational Mechanics Group (GIMC) is organizing the
Minisymposium “Novel Approaches in Computational Mechanics” at the AIMETA
Congress, in Palermo on September 4-9, 2022.

T

A special session will be devoted to the presentation of the PhD theses in
Computational Mechanics winner of the GIMC award 2021.
A special session will be organized for young researchers in which the best
presentation will be awarded.
All the details can be found at the web site:www.pa22.aimeta.it

GIMC Best PhD Thesis Award
The GIMC Best PhD Thesis Awards committee, composed by Umberto Perego(Politecnico di Milano), Maria Laura de Bellis (Università degli Studi "Gabrieled'Annunzio" Chieti-Pescara), Antonia Larese (Università di Padova) and Michele
Marino (Università di Roma Tor Vergata), selected the following theses, defended in
2021, as the best Theses in Computational Solid and Fluid Mechanics:
Dr. Donatella Passiatore, Politecnico di Bari, “Direct Numerical Simulations ofhypersonic turbulent boundary layers with thermochemical non-equilibrium effects”.
This thesis was also selected as candidate to the ECCOMAS PhD Award
Dr. Alessia Patton, Università di Pavia, “Advanced isogeometric methods with afocus on composite laminated structures”.
iacm expressions 50/22 18

for all inclusions under GIMC please contact
Sonia Marfia
sonia.marfia@uniroma3.it

GIMC (Gruppo Italiano di Meccanica Computazionale) andSIMAI (Società Italiana di Matematica Applicata e Industriale) are glad
to announce that their first joint workshop for young scientists (≤ 35-year-old)will take place in Pavia on September 29-30, 2022.
The meeting aims at providing participants with a platform to exchange their mostrecent results, to keep up-to-date on new trends and to foster their interactions.
The program includes two plenary lectures delivered by leading scientists, asession dedicated to the recipients of the GIMC-SIMAI young awards, parallelthematic sessions organized by the participants, as well as contributed sessions.
Contributions are welcome from all areas of applied mathematics andcomputational mechanics.
The initiative is also supported by the ECCOMAS Young Investigators Committee.
Follow the committee on Facebook, Instagram, Twitter, LinkedIn.
All the details can be found at the web site:www.gimcsimaiyoung2022.unipv.it l

19 iacm expressions 50/22

BMEC, the Brazilian Association for Computational Methods in Engineering,is celebrating this year its 25th anniversary. Since its foundation, on September
15, 1997, ABMEC has continuously increased its number of members and promoted
the importance and the novelty issues of the Computational Mechanics area in
Brazil.

A

In the last two years, similarly to the rest of the world, the effect of the COVID-19
crisis on the Brazilian scientific community has been profound. Nevertheless,
ABMEC could promote two very successful congresses, both in the virtual
format. In 2020, the XLI CILAMCE (XLI Ibero-Latin American Congress on
Computational Methods in Engineering)
was bravely organized under the chairof Prof. Aref K. L. Kazan from theFederal University of the Latin-American
Integration – UNILA). The congress
took place within a virtual platform
specially designed for CILAMCE-2020.
Despite the enormous challenge inorganizing, for the first time, aconference in the virtual format,
CILAMCE-2020 was a tremendous
success, receiving 640 participantsfrom all parts of Brazil, Latin America,
and a few other colleagues from farther
abroad. l

III-Pan-American Congress on
Computational Mechanics
n 2021, the third PANACM(III-Pan-American Congress on
Computational Mechanics) was
organized by ABMEC, for the firsttime jointly with CILAMCE in its XLII
edition.

I

The format was still virtual, but astudio based in Rio de Janeiro wasused to broadcast the live participationof the plenary speakers, coordinatedby the chairman Prof. Alvaro Coutinho
from the Federal University of
Rio de Janeiro.
The event's success wasdemonstrated in the 555 pre-recorded
talks, 48 mini-symposia, and 662
participants from Brazil and several
parts of the world. l

iacm expressions 50/22 20

for all inclusions under ABMEC please contact
Felício Bruzzi Barros
feliciobbarros@gmail.com

ow that the pandemic crisis seems to be under control, we are very excitedabout the optimistic prospects of a year of in-person reunions. The milestoneof the Brazilian Computational Mechanics community is the fully in-person
XLIII CILAMCE, which will be held from November 21 to 25 in Foz do Iguaçu,
Paraná, Brazil. The organization is under the chair of Profs. André Jacomel Torii
and Marco André Argenta. CILAMCE is a multidisciplinary event: scientists
and engineers from all fields are encouraged to attend the conference. The
technical program typically includes worldly prominent plenary speakers andseveral mini-symposia parallel sessions with contributed papers. In this edition,
CILAMCE will also include novel social activities of interaction and scientificdissemination on the last day ofthe event.

N

The web page of the event ishttps://www.cilamce.com.br/ and thedeadline for full paper submission is
July 11, 2022. Upon request abstracts may
still be submitted.
You are very welcome to join us at theworld´s wonder of Iguaçu Falls for theupcoming XLIII CILAMCE! l

21 iacm expressions 50/22

MECOM 2021
XXXVII Argentine Congress on Computational Mechanics
Resistencia, Chaco, Argentina
1 – 5 November 2021
he XXXVII Argentine Congress on Computational Mechanics (MECOM 2021) took
place from November 1st through November 5th, 2021, in the city ofResistencia, Chaco, Argentina. This new edition of the annual AMCA Congresses
was organized in fully virtual mode by the Computational Mechanics Laboratory(LAMEC), dependent on the Faculty of Engineering of the National University of theNortheast (UNNE) and the National Scientific and Technical Research Council(CONICET), together with the Argentine Association for Computational Mechanics(AMCA).

T

The Organizing Committee included Prof. Javier L. Mroginski (Chairman),Prof. José Basterra, Prof. Pablo Beneyto, and Prof. Juan M. Podestá (General
Coordinator). The Scientific Commitee was chaired by Prof. Hugo G. Castrowith Dr. Rodrigo R. Paz and Prof. Gustavo A. Aucar as Co-Chairmen andProf. Mario E. De Bortoli as General Coordinator.
The Congress hosted ten Invited Lecturers, Prof. Deborah Sulsky (University of
New Mexico, USA), Prof. Rainald Löhner (George Mason University, USA),

Figure 1:
MECOM 2021 staff at the Faculty of Engineering of
the National University of the Northeast (UNNE)

Figure 2:
Prof. Guillermo Etse and Prof. Paul Steinmann

Figure 3:
Prof. Rainald Lohner and Prof. Sergio Oller

Figure 4:
Participants of the Congress during Q&A of a session

iacm expressions 50/22 22

for all inclusions under AMCA please contact:
Felício Bruzzi Barros
feliciobbarros@gmail.com
Prof. Sergio Oller (National University of Salta, Argentina), Prof. Norman Fleck(Cambridge University Engineering Department, UK), Prof. Armando Awruch,(CNPq Researcher, Brasil), Prof. Martín Idiart (National University of La Plata,
Argentina), Prof. Gustavo Buscaglia (University of São Paulo, Brazil),
Prof. Pedro Arduino (University of Washington, USA), Prof. Matteo Parsani (King
Abdullah University of Science and Technology, Saudi Arabia) and Prof. Paul
Steinmann (University of Erlangen-Nürnberg, Germany). Also, three Keynote
Lectures were delivered by Prof. Laura Morales (University of Buenos Aires,
Argentina), Prof. Alex Ferrer (Polytechnic University of Catalonia, Spain), andProf. Mauro Innocente (Coventry University, UK). Most of these lectures areavailable on the Congress’ YouTube channel: https://bit.ly/MeCOM2021.
Full-length papers were submitted to a peer review process prior to publication.
Accepted papers have been published in the proceedings series “Mecánica
Computacional”, which are openly available at the following website:
http://www.amcaonline.org.ar/mcamca. The Conference consisted of 21 TechnicalSessions with more than 230 papers presented.
A special session was devoted to undergraduate students, with awards for bestposters, that were granted to:1.
Rodrigo R. Velazquez, from the National University of Córdoba, Argentina.
2.
Lucas J. Donnet and Maximiliano O. Frutos
from the National Technological
University (UTN) at Santa Fe, Argentina.
3.
Arthur M. Carvalho, from the Federal
University of Unipampa, Brazil.
The Congress also included other activities
like the X Meeting of OpenFOAM users
and the special session to honor
Prof. Armando M. Awruch for his retirement.

MECOM 2021 logo

CALL FOR PAPERS
The Argentine Association for Computational Mechanics (AMCA) announces the
XXXVIII Argentine Congress on Computational Mechanics (MECOM 2022),
to be held in Bahía Blanca, Argentina, organized by Grupo de Investigación en
Multifísica Aplicada (GIMAP), of the Universidad Tecnológica Nacional (UTN),
Universidad Nacional del Sur (UNS) and AMCA. The following speakers havealready confirmed their participation at the MECOM 2022: Marian Wiercigroch(University of Aberdeen, Scotland), Tim Collonius (Caltech, USA), Marcelo Savi
(Universidade Federal do Rio de Janeiro, Brazil), Alper Erturk (Georgia Institute ofTechnology, USA) and Elbio Palma (Universidad Nacional de Sur, Argentina).
https://amcaonline.org.ar/mecom2022/
23 iacm expressions 50/22

USACM Elects
New Executive Committee Members

U

SACM recently held its elections for one new officer and four members-at-large.
The terms begin August 1, 2022. The results are as follows:
Secretary/Treasurer:
Yongjie Jessica Zhang, Carnegie Mellon University
The Secretary/Treasurer will rotate to the office of President
during the next four years.

Members-at-Large:
The members-at-large serve a four-year term on the Executive Committee

John Evans

Hector Gomez

David Littlewood

Lucy Zhang

University ofColorado at Boulder

Purdue University

Sandia National
Laboratories

Rensselaer
Polytechnic Uni.

Virtual Thematic Conference on the Role of Mathematicaland Computational Modeling in Cancer Research
The USACM Technical Thrust Area (TTA) on Biological Systems, with support fromthe Oden Institute for Computational and Engineering Sciences at the University ofTexas, hosted a virtual Thematic Conference on the Role of Mathematical andComputational Modeling in Cancer Research, January 10-11, 2022. Organized byDrs. Prashant K. Jha, Ernesto A.B.F. Lima, and Chengyue Wu, all from the Oden
Institute, the conference brought leading computational oncology experts together in
one platform to share their work and vision for integrating quantitative biological datawith mechanistic mathematical models. Invited presentations were made by:Professors Luciana R C Barros, Heiko Enderling, David Fuentes, Hector Gomez,
John D. Hazle, Mohit Kumar Jolly, John Lowengrub, J. Tinsley Oden, Savannah
Partridge, Katarzyna Rejniak, Russell Rockne, and Thomas Yankeelov. Several shorttalks were also presented from abstract submissions. Contact the USACM office atadmin@usacm.org for information about how to access videos of these talks.
TTA Virtual Seminars
Several TTAs (Technical Thrust Area) of the USACM have been holding monthly virtual seminars which have featured guest speakers and discussions on various topics ofimportance to their field. Videos of the talks may be found at usacm.org/ttas under
the TTA of interest. The following talks have taken place since September 2021:
TTA on Computational Fluid Dynamics and Fluid Structure Interaction
Yuri Bazilevs, (Brown University), Advanced and Practical Fluid-Structure Interaction
Tayfun Tezduyar (Rice University), Space-Time Computational Analysis: From Inception to
New Generations

Marilyn Smith (Georgia Tech), High-Fidelity Aeroelasticity Simulations for Rotating Systems
iacm expressions 50/22 24

for all inclusions under USACM please contact
admin@usacm.org
Michael Sprague (National Renewable Energy Laboratory), Predictive Simulations of Wind Turbines on Next-Generation
Supercomputers

Lucy Zhang (Rensselaer Polytechnic Institute), Numerical Coupling Workflow of Multiphysics Interactions Using Immersed
Methods

Arif Masud (University of Illinois at Urbana-Champaign), Variational Multiscale Discontinuous-Galerkin (VMDG) Method for Couple
Fluid-Solid Systems: Theory and Applications

Guglielmo Scovazzi (Duke University), The Shifted Boundary Method for CFD
TTA on Large Scale Structural Systems and Optimal Design
Evgueni T. Filipov (University of Michigan), Simulating Shape Morphing Origami: Kinematics, Mechanics and Multi-Physics
Matthew J. Zahr (University of Notre Dame), High-Order and Reduced-Order Methods for Improved Engineering Analysis andDesignJifu Tan (Northern Illinois University), Multiphysics Modeling of Biological Flows with Cell Suspensions
Narasimha Boddeti (Washington State University), Optimal Design and Manufacture of Architected Fiber Reinforced Composites
Som L. Dhulipala (Idaho National Laboratory), Active Learning for Accelerating Expensive Computational Tasks in an OpenSource High-Performance Computing Platform

Masoud Behzadinasab (Brown University), Peridynamics for Simulation of Fracture Mechanics Problems.
TTA on Uncertainty Quantification and Probabilistic Modeling(see TTA website for more information on speakers before September 2021)
Yeongjong Shin, Brown University (Discussant: Dongbin Xiu), Mathematical Approaches for Robustness and Reliability in
Scientific Machine Learning

Catherine Gorlé Stanford University (Discussant: Gianluca Iaccarino), Uncertainty Quantification and Data Assimilation for
Predictive Computational Wind Engineering

Audrey Olivier, USC (Discussant: Lori Graham Brady), Bayesian Learning of Neural Networks for Small or Imbalanced Data Sets
Johann Guilleminot, Duke University (Discussant: Roger Ghanem), Stochastic Modeling for Physics-Consistent Uncertainty
Quantification on Constrained Spaces, with Various Applications in Computational Mechanics

Danial Faghihi, University at Buffalo (Discussant: J. Tinsley Oden), Toward Selecting Optimal Predictive Computational Models
Ioannis Kougioumtzoglou, Columbia University (Discussant: George Deodatis), Path Integrals in Stochastic Engineering
Dynamics

Jian-xun Wang, University of Notre Dame (Discussant: Michael Brenner), Leveraging Physics-Induced Bias in Scientific Machine
Learning for Computational Mechanics – Physics-Informed, Structure-Preserved Learning for Problems with Complex Geometries l

USACM Upcoming Events
further details at usacm.orgmmm

Meshfree and Novel Finite Element Methods with Applications
September 25-27, 2022, Berkeley, CA (mfem2020.usacm.org)
IGA 2022, November 6-9, 2022, Banff, Canada (iga2022.usacm.org)
17th U.S. National Congress on Computational Mechanics
July 23-27, 2023, Albuquerque, New Mexico (17.usnccm.org) l
25 iacm expressions 50/22

SFB/TRR 339 – Digital Twin of the Road System
he mobility of people and goods is a central basis of our modern society withincreasingly global and diverse networked processes. In its present form,
mobility, especially with regard to road traffic, is currently confronted with global
challenges (durability, safety, efficiency, ecology, costs, automation etc.) thaturgently require fundamental solutions.
In the SFB/TRR 339 (TU Dresden, RWTH Aachen, speaker: Prof. Michael Kaliske,
Insitute for Structural Analysis, TU Dresden), a spatially and temporally multi-dimensional, digital/virtual image (reality model in space and time) of vehicle, tires and roadsurface (concrete and asphalt) taking into account the road pavements (integrated
multi-functionality) will be developed and researched. The reality model combines
all available and relevant information about the "Road of the Future" from physicalinvestigations and modeling as well as from informational and traffic data (sensor
data, data models etc.). It enables and requires the interaction between the physicalstructural and the informational-traffic design level. This interactive reality model inspace and time is called the digital twin of the road system and is used in perspective
to analyze, control and forecast the physical original (real road system consisting ofvehicle, tires, lane, nearby street space) by means of common interfaces. The
advancement of the road to a high-tech platform will be developed using the new,interdisciplinary research approach (civil engineering, computer science, society).
The research approach is based on a three-stage development strategy:
In Phase I, the required submodels are designed and developed.
In Phase II, the partial models are combined using common, standardized interfaces
and integrated into a holistic model of the road system (digital shadow), which inPhase III will allow the analysis and control of the road system with its own control
components (digital twin).
The aim is to achieve the vehicle's present high level of development in a similar way
for the road system, so that a new quality of integration of vehicles and infrastructure
can be achieved. The overriding aspects of law and sustainability shall be explicitly
included into the conception and development of the digital twin of the road systemfrom the beginning on. The digital twin of the road system will lead to an intelligent,
gentle and sustainable use of the road infrastructure. Other expected results are
groundbreaking condition forecasts, interfaces to local traffic control, the optimal
synthesis of building materials and structures, interfaces to automated driving and the
reduction of emissions, e.g. by minimizing traffic jams or durable road infrastructurecomponents (use of resources). l
Further information:

iacm expressions 50/22 26

for all inclusions under gacm please contact:
Norbert Hosters
gacm@gacm.de

DFG SPP 2311 - Robustly coupling continuumbiomechanical in silico models to obtain active biologicalsystem models for later use in clinical applications
he enormous potential of in silico (computer-aided) models is still little utilized in
medicine. As in other high-tech fields, this potential could be exploited more
intensively to accelerate the development of modern diagnostic and individualized
treatment methods. Most existing in silico models are limited to single scales (e.g.
cell, tissue) or to generic multi-scale models of single organs (e.g. muscle, liver).
However, to understand symptoms and disease, multiple scales of size and timemust be considered in the context of the entire system. The complex cross-scale
relationships are often very difficult to identify without a systematic approach andcomputational models. Therefore, a central task of numerical biomechanics andbiomedical research is to develop robust coupling methods and strategies. These
must integrate the scales of biological systems from the molecule to the complete
organ system or organism.
In order to promote this emerging research field, theGerman Research Foundation (DFG) has approved
a new Priority Programme (SPP2311) on the“Robust Coupling of Continuum-biomechanical In
Silico Models to Establish Active Biological System
Models for Later Use in Clinical Applications –Co-design of Modelling, Numerics and Usability”.
The programme consists of 11 research projects,
each of which being a collaborative effort ofinterdisciplinary, Germany-wide research groups
from medicine, mathematics, computer scienceand engineering. The programme is coordinatedby Prof. Oliver Röhrle, PhD, Director of the Institutefor Modelling and Simulation of Biomechanical
Systems at the University of Stuttgart.
The overall aim of the SPP is to further existing
methodologies to achieve a new generation ofrobust biomechanical models that ultimately can beused in clinical practice. In that sense, the SPP aims
to develop the enabling methodologies to build newcomplex models for clinical applications, but does
not aim to transfer these new models to clinics via
clinical studies, yet. Thus, the SPP understands
itself as a methodically oriented programme focusing on computational biomechanicalmodels of active systems for medical questions.
The SPP is divided into three closely interrelated areas. The area "Modeling of
coupled biomechanical systems" deals with the coupling of biomechanical in silico
models for the description of active biological systems on different length, time
and/or function scales or with the coupling of continuum biomechanical organsystem models. The area "Numerics of coupled biomechanical systems" focuseson mathematical algorithms for robust, numerical solution of coupled models for
active biological systems as well as their implementation on workstations andsupercomputers. And finally, the area "Usability" deals with data exchange, validationand preparation of numerical continuum biomechanical models for answering
clinically relevant questions and for person-specific solutions. l
Further information:
27 iacm expressions 50/22

he Japan Association for Computational Mechanics (JACM) is a union ofresearchers and engineers working in the field of computational mechanics
mainly in Japan. The JACM is a loosely coupled umbrella organization covering
29 computational mechanics related societies in Japan through communication
with e-mail and web page (https://ja-cm.org/index-e.html). The number of individual
members is about 330. JACM members actively participate in the IACM activities.

T

Figure 1:
Group Photo offirst seminar held
on December 10th 2021

On December 10th, 2021, the first JACM awardee seminar was held online. The
purpose of this seminar series is to have young researchers who received the
JACM awards recently give lectures on their latest research activities. Two lectures
were given in the seminar. The first one entitled “A Nestable, Multigrid-friendly Gridon a Two-dimensional Sphere with an Exact Quadrature Rule” was presented by

The Computational Mechanics Symposium
he Computational Mechanics Symposium is an annual event that has been
held in December every year for 10 years, since 2011. Young researchers
representing computational eight mechanics related professional societies
presented. On Monday, December 6, 2021, JACM took a part of the 11th
Computational Mechanics Symposium. The symposium is organized by the
Science Council of Japan (SCJ) in association with eight computational mechanics
related academic societies in Japan. The function of SCJ is defined as “TheScience Council of Japan was established in 1949 as a “special organization”
under the justification of the Prime Minister, operating independently of thegovernment for the purpose of promoting and enhancing the field of science, andhaving science reflected in and permeated in administration, industries and people’s
lives. It represents Japan’s scientists both domestically and internationally …”(http://www.scj.go.jp/en/scj/index.html). Due to the pandemic of the Covid-19,

T

Figure 3:
Professor Yuto Otoguro

iacm expressions 50/22 28

For all inclusions under JACM news please contact:
Hiroshi Okada jim@ja-cm.org

Dr. Daisuke Hotta (Japan Meteorological Agency), who is the JACM Young
Investigator Award (YIA) winner of year 2021. The second one was “Hierarchical
Domain Decomposition Method, and Its Application to the Parallel Electromagnetic
Field Analysis” given by Professor Shin-ichiro Sugimoto (Hachinohe Institute ofTechnology), received the YIA of year 2019.
Following the successful first seminar, the second seminar was held on March 17th,
2022. Professor Kazuya Shibata (University of Tokyo), the YIA winner of year 2018,gave the lecture entitled “Multi-resolution Particle Methods for Fluid Analysis andTheir Applications in Engineering.” Then, Professor Shunhua Chen (Sun Yat-sen
University), received the YIA of year 2021, presented the lecture of “Fracture
Simulations of Laminated Composite Structures Subjected to Various Loading
Conditions.”
The JACM plans to hold this seminar series twice or three times a year in
the future. l

Figure 2:
Group Photo of
second seminar heldon March 17th 2022

the symposium was held in a mixed face-to-face/online hybrid format.

* In the precious issue,
the name of Professor
Yoshihiro Tomita(Emeritus Professor of
Kobe University,
Specially Appointed
Professor of Kyoto
Institute of Technology)
was missing from the listof newly inducted
Honorary Members of
JACM.
We would like toapologize for making
this editorial error.

Professors Seiichi Koshizuka (UTokyo) and Kazuo Furuta(UTokyo) served as thegeneral chairs. After the opening remark by Professor Shinobu Yoshimura (UTokyo),eight young researchers representing the participating computational mechanics
related societies presented their latest research accomplishments. Majority ofthem were the recent award recipients of the participating societies. Professor
Yuto Otoguro (Tokyo Univ. Science, Photo 3) was the recipient of the 2021 JACM
Young Investigator Award. He gave a talk “Space–Time Computational Methods
and Isogeometric Discretization for Flow Problems with Complex Geometries andMoving Boundaries and Interfaces”. After the talks by the young researchers,
Professors Keiko Takahashi (Waseda Univ.) and Yasuyuki Takata (Kyushu Univ.)
delivered key-note presentations on issues related with SDGs (Sustainable Growthand Developments) were given by followed by discussions. l

29 iacm expressions 50/22

for all inclusions under SEMNI please contact
Elias Cueto
ecueto@unizar.es

SEMNI changes its nameuring the last general assembly of SEMNI, held on March 23th, 2022, oursociety agreed on changing its name. Soon after our 30th anniversary, theexecutive committee felt that the original name of our society (Sociedad Españolade Métodos Numéricos en la Ingeniería—Spanish Society for Numerical Methods inEngineering) no longer reflects many of the activities of our members. A proposal
was elevated to the general assembly to change
the name of our society. The proposal suggested
the name “Sociedad Española de Mecánica e
Ingeniería Computacionales” (Spanish Society ofComputational Mechanics and Computational
Engineering).

D

This proposal was approved by the general
assembly, together with the proposal of maintaining,for historical reasons, the acronym of the society,
SEMNI.
All this is already reflected in the new web page,www.semni.org. l

SEMNI Juan C. Simó award 2021
EMNI has awarded its annual Juan C. Simó prize during its last executive
meeting. The awardee will receive his prize during the gala dinner ceremonyof the joint Spanish-Portuguese Conference on Numerical Methods, CMN, to beheld in Las Palmas de Gran Canaria next September 2022.

S

SEMNI’s prize to young researchers commemorates the legacy of the lateProfessor Juan C. Simó in the field of numerical methods. The committee
was formed in this occasion by Kerstin Weinberg (Department of MechanicalEngineering, Universität Siegen), Olivier Allix (Laboratoire de Mécanique etTechnologie, ENS Paris-Saclay) Jose Paulo Moitinho de Almeida (Department
of Civil Engineering, Architecture and Georesources of Instituto Superior Técnico,
Universidade de Lisboa) and Nicolas Moës (GeM Institute, Ecole Centrale deNantes). The president of the committee was prof. Juan José Ródenas García,
acting on behalf of SEMNI’s executive committee.
Figure 1:
Prof. Matteo Giacomini

In this occasion, the awardee is Prof. Matteo Giacomini (UPC´-BarcelonaTech),for his contributions to computational nonlinear solid and fluid mechanics. The
Committee has valued the breadth of his research activities and the variety andextent of his national and international research collaborations. l

SEMNI best PhD award 2021
he SEMNI best PhD thesis award committee, composed by Elías Cueto(Universidad de Zaragoza, president, acting on behalf of SEMNI’s executive
committee), José Antonio Sanz Herrera (University of Seville), Salvador Izquierdo(Aragon Institute of Technology, ITAInnova), and Santiago Badía (Monash
University, Australia).

T

Figure 2:
Dr. David Codony
iacm expressions 50/22 30

The recipient of this year’s best thesis award has been Dr. David Codony, for histhesis entitled “Mathematical and computational modelling of Flexoelectricity atmesoscopic and atomistic scales”, developed under the supervision of prof. Irene
Arias at UPC-BarcelonaTech. lfor all inclusions under CEACM
please contact
Adnan Ibrahimbegovic
adnan.ibrahimbegovic@utc.fr

2021 News from CEACM
URL: http://www.gf.unsa.ba/web/
he CEACM Central European Association for Computational Mechanics, hascreated new CEACM Awards to honor active researchers from CEACM partnercountries, regrouping (in alphabetical order) Austria, Bosnia and Herzegovina,
Croatia, Czech Republic, Hungary, Slovakia and Slovenia. In particular, the seniorscientists are honored by newly created CEACM Prof. Mang Award and young
researchers by CEACM Best Thesis Award. The highest honor by CEACM isnamed after its founding father Herbert Mang, Professor at TU Wien, and its first
President 1992-99. The CEACM Prof. Mang Award is bestowed each year tohonor an outstanding individual who has demonstrated sustained efforts inadvancing either theoretical or practical aspects of computational mechanics andwho provided strong leadership either in an academic environment or in different
industrial applications.

T

The CEACM Young Researcher Award was initiated by CEACM to honor
an exceptional contribution of a young scientist, who has completed
his/her doctoral studies in the broad domain of Computational
Mechanics within the year of the award call. The first CEACM Awards
were attributed in the meeting of the CEACM Executive Board that was
reunited on March 11, 2022 in online meeting (Figure 1).
Figure 1:
CEACM Board Online Meeting, March 11, 2022

Among the CEACM laureates are Vladimír KOMPIŠ (Figure 2) and Adnan
IBRAHIMBEGOVIC (Figure 3) for CEACM Prof. Mang Award, and Henrik T. Sykora(Figure 4) for CEACM Best Thesis Award.

Figure 2:
Vladimír KOMPIŠ
Past-President CEACM
CEACM Prof. Mang Award

Figure 3:
Adnan Ibrahimbegovic
President CEACM
CEACM Prof. Mang Award

Figure 4:
Henrik T. Sykora
CEACM Best Thesis Award

The current and future scientific activities of CEACM concern two CEACM
sponsored scientific conferences. The first conference that will take place 28-30
September 2022 in Croatian City of Pula is 10th ICCSM Int. Congress of CroatianSociety of Mechanics. The main contact is CEACM Board Croatian representative,
Prof. Zdenko Tonković (e-mail: zdenko.tonkovic@fsb.hr). Within the framework ofthis meeting, there is a special session in honor of Professor Jurica Sorić, past
CEACM president and longtime Croatian representative in the CEACM Board.
This session, co-organized by Adnan Ibrahimbegovic, current CEACM president,
Bernhard Pichler, past CEACM president, and Zdenko Tonković, is entitled‘Advanced Numerical Methods in Structural Analysis:
Recent Developments and Future Challenges’, seeking to gather the contributions
from different domains of expertise of Prof. Sorić. More information is given at
meeting website: www.iccsm2021.fsb.hr.
The second conference is ECCOMAS MSF 2023, the 6th International Conference
on Multi-scale Computational Methods for Solids and Fluids, which will take placein June 28-30, 2023. This conference is already 6th in a long series is jointly
organized by University of Sarajevo, Academy of Sciences and Arts of Bosnia andHerzegovina and UT Compiegne. More information is given at meeting website:www.gf.unsa.ba/eccomas-msf-2023.
We sincerely hope to welcome you at each of these two meeting that should takeplace onsite, as opposed to the current practice of (virtual) online meetings, or atleast in hybrid format that was used in organizing ECCOMAS MSF 2021 meeting(website: www.gf.unsa.ba/eccomas-msf-2021).
31 iacm expressions 50/22

Towards a post-COVID era
he Japan Society for Computational Engineering and Science (JSCES) was
founded as a voluntary association in 1995 following the success of the 3rdWorld Congress on Computational Mechanics (WCCM III), which was held in Chiba,
Japan, in 1994. The society became a General Incorporated Association in 2010
owing to the hard work of our pioneers who constituted bylaws and regulations
from scratch and revised and maintained them according to social needs. Thanks
to our distinguished senior members, the governing structure of the JSCES has
now reached a stable state. However, the COVID-19 pandemic has in the past
few years enforced minimization of social activities worldwide, which of course
has largely affected the activities of the JSCES as well. I believe that it is now
time to return to the starting point and rethink what engineers are really for, how
an academic society should act and support them, and how the society shouldhave a relationship with the surrounding communities. It is true that the progressin the fields of computational engineering and computational mechanics is now
more known to the public than before; however, it is still very important for the
JSCES to promote and accelerate activities that match social and industrial needs
by enhancing the information transmission capacity as one of the largest responsible
incorporated associations in this field.

T

Under these circumstances, I would like to raise the following three main policies formy term of office:(a) Endorsement of engineers in the fields of computational engineering/
computational mechanics and promotion of international activities
The JSCES will support and promote engineers, including younger membersin the fields of computational engineering/computational mechanics, throughannual conferences, study groups, short courses, and summer camps.
In addition, the JSCES will manage funds aiming to support the members
participating in international conferences organized by the IACM and willorganize, co-organize, and support conferences, such as the WCCM and theCOMPSAFE.
(b) Enhancement and reinforcement of information transmission capacity as a
responsible incorporated association
The JSCES will publish high-quality transactions, academic journals, and books
as one of the largest responsible incorporated associations in this field.
(c) Promotion and acceleration of activities that match social and industrial needs
The JSCES will secure transparency and fairness in its administration to betrusted by communities worldwide and cooperate with other academic
societies to promote and accelerate theactivities.
It is perhaps a difficult time now to enhanceacademic activities, but the JSCES will keep
providing fruitful information and will actively
offer physical (or virtual, but interesting)
events to proceed to the next step after
this pandemic.

Professor Daigoro Isobe,
the 14th presidentof JSCES
iacm expressions 50/22 32

byDaigoro Isobe.

The 27th JSCES Annual Conference
he 27th JSCES Annual Conference on Computational Engineering andScience, chaired by Prof. H. Hasegawa (Shibaura Inst. Tech.), was held
during June 1–3, 2022, in an in-person/online hybrid format. The in-person
event was held at Akita City NIGIWAI Interchange Center AU, Akita, Japan.
About 630 participants attended the conference, and about 300 papers with full
lectures were presented. The two-thirds of papers were presented in theface-to-face style, and the rest were online. Fruitful discussions were exchanged
in 28 organized-sessions associated with a plenary lecture, graphic awards andfive luncheon seminars. Special seminars were also organized at lunch times toinspire graduate students in the field of computational mechanics by offering
small talks from three young professors and researchers.

T

The banquet was held on the evening of the second day. The NAMAHAGE
show, which is the traditional performance by demon-like beings in Akita, wasdemonstrated. Participants enjoyed the first in-person banquet in a long time.

The JSCES Grand Prize 2021
n the second day of the conference, a plenary lecture entitled “Modelingand Simulation of Complex Coupled Multifield Problems – from Basic Research
to Advanced Applications” was delivered by Prof. Wolfgang A. Wall, the Funding
Director of Institute for Computational Mechanics & Center for Computational
Biomedical Engineering, Technical University of Munich, Germany (Figure 2).
In the lecture, he presented the numerical study on powder based metal additive
manufacturing, the assessment of all-solid-state batteries based on the electrochemo-thermo-mechanics framework, and the advanced basic research on tissuelevel to powerful full organ models.

O

Prof. Wolfgang A. Wall received “The JSCES Grand Prize 2021”, for his outstanding
contributions in the field of computational engineering and sciences, at the following
ceremony (Figure 3).

33 iacm expressions 50/22

conference diary planner
31 Jul - 5 Aug 2022 WCCM-APCOM 2022 - 15th World Congress on Computational Mechanics &8th Asian Pacific Congress on Computational Mechanics
Venue:
Yokohama, Japan
Contact:
https://www.wccm2022.org
4 - 9 Sept 2022 AIMETA - Italian Association of Theoretical and Applied Mechanics
Venue:
Palermo, Sicily, Italy
Contact:
https://www.aimeta.it/
12 - 14 Sept 2022 CMN 2002 - Congress on Numerical Methods in Engineering
Venue:
Las Palmas, Spain
Contact:
https://congress.cimne.com/cmn2022/
25 - 27 Sept 2022 MFEM2020 - Meshfree and Novel Finite Elements with Applications
Venue:
Berkeley, CA, USA
Contact:
mfem2020.usacm.org
6 - 9 Nov 2022 IGA2022 - USACM Thematic Conference - Isogeometric Analysis
Venue:
Banff, Canada
Contact:
iga2022.usacm.org
1 – 4 Nov 2022 MECOM 2022 - XXXVIII Argentine Congress on Computational Mechanics
Venue:
Bahía Blanca, Argentina
Contact:
https://amcaonline.org.ar/mecom2022/
2 - 4 Nov 2022 5th AfriComp - 5th African Conference on Computational Mechanics
Venue:
Cape Town, South Africa
Contact:
https://africomp.info/
6 - 9 Nov 2022 IGA 2022 - 10th International Conference on Isogeometric Analysis
Venue:
Banff, Canada
Contact:
iga2022.usacm.org
21 - 25 Nov 2022 XLIII CILAMCE - XLIII Ibero-Latin American Congress on Comp Methods in Engineering
Venue:
Foz do Iguaçu, Brazil
Contact:
https://www.cilamce.com.br/
6 Dec 2022 11th JACM Computational mechanics Symposium
Venue:
Japan
Contact:
https://ja-cm.org/index-j.html
5 - 8 March 2023 KomPlas Tech 2023 - XXVIII Conference on Computer Methods in Materials Technology
Venue:
PolandContact:
https://www.eccomas.org/conferences/
30 - 31 March 2023 SYMCOMP 2023 - VI Int Conf on Numerical 6 Symbolic Computation: Dev & Applications
Venue:
Évora, Portugal
Contact:
https://www.eccomas.org/conferences/
25 - 28 April 2023 CFC 2023 - 22nd IACM Computational Fluids Conference
Venue:
Cannes, France
Contact:
https://cfc2023.iacm.info/
30 May - 1 June 2023 Math 2 Product (M2P) - 1st Emerging Tech in Comp Science for Industry, Sustainability & Innovation
Venue:Taormina (Messina), Italy
Contact:
https://m2p2023.cimne.com/
1 - 3 June 2023 EUROGEN 2023 - 15th Int Conf on Evolutionary & Deterministic Methods for Design, Optimization & Control
Venue:
Chania, Crete, GreeceContact:
https://www.eccomas.org/conferences/
5 - 7 June 2023 COUPLED PROBLEMS 2023 - Int Conf on Coupled Problems in Science & Engineering
Venue:
Chania, Crete Island, Greece Contact:
https://coupled2023.cimne.com/
12 - 14 June 2023 COMPDYN 2023 - IX Int Conf on Comp Methods in Structural Dynamics & Earthquake Engineering
Venue:
Athens, GreeceContact:
https://www.eccomas.org/conferences/
12 - 14 June 2023 UNCECOMP 2023 - V Int Conf on Uncertainty Quantification in Computational Sciences & Eng
Venue:
Athens, Greece Contact:
https://www.eccomas.org/2022/04/01/thematic-conference-2023/
18 - 21 June 2023 IGA 2023 IX International Conference on Isogeometric Analysis
Venue:
Lyon, France
Contact:
https://www.eccomas.org/conferences/
19 - 21 June 2023 ADMOS 2023 - International Conference on Adaptive Modeling & Simulation
Venue:
Gotheburg, Sweden
Contact:
https://admos2023.cimne.com/
21 - 23 June 2023 CFRAC 2023 - The 7th Int Conf on Comp Modeling of Fracture & Failure of Materials & Structures
Venue:
Prague, Prague, Czechia
Contact:
https://www.eccomas.org/conferences/
25 - 27 June 2023 MSF 2023 - 6th International Conference on Computational Methods forSolids & Fluids
Venue:
Sarajevo, Bosnia-Herzegovina Contact:
https://eccomas.gf.unsa.ba/msf-2023/
27 - 29 June 2023 MARINE 2023 - X International Conference on Computational Methods in Marine Engineering
Venue:
Madrid, Spain
Contact:
https://marine2023.cimne.com/
25 - 27 June 2023 MSF 2023 - V International Conference on Computational Methods for Solids & Fluids
Venue:
Sarajevo, Bosnia-HerzegovinaContact: https:// eccomas.gf.unsa.ba/msf-2023
3 - 6 July 2023 SMART 2023 - IX Conference on Smart Structures and Materials
Venue:
Patras, GreeceContact:
https://www.eccomas.org/conferences/
5 - 7 July 2023 ICCCM 2023 - 7th International Conference on Computational Contact Mechanics
Venue:
Torino, Italy https://www.eccomas.org/conferences/18 - 21 July 2023 CMP4 2023 - Computational Modelling of Multi-Uncertainty & Multi-Scale Problems
Venue:
Porto, Portugal
Contact:
https://www.eccomas.org/conferences/
23 - 27 July 2023 17th U. S. National Congress on Computational Mechanics
Venue:
Albuquerque, NM, USA
Contact:
17.usnccm.org
24 - 28 July 2023 MULTIBODY 2023 - ECCOMAS Thematic Conference on Multibody Dynamics
Venue:
Lisboa, Portugal
Contact:
https://www.eccomas.org/conferences/
25 - 29 July 2023 USNCCM16 - 16th U.S. National Congress on Computational Mechanics
Venue:
Chicago, Illinois
Contact:
http://16.usnccm.org/
26 - 28 July 2023 SIM-AM 2023 - IV International Conference on Simulation for Additive Manufacturing
Venue:
Munich, Germany
Contact:
https://www.eccomas.org/conferences/
20 - 23 Aug 2023 M-FET 2023 - III Modern Finite Element Technologies – Mathematical & Mechanical Aspects
Venue:
Mülheim, Germany
Contact:
https://www.eccomas.org/conferences/
5 - 7 Sept 2023 COMPLAS 2023 – XV!! Int. Conference on Computational Plasticity
Venue:
Barcelona, Spain
Contact:
https://complas2023.cimne.com/
24 - 27 Sept 2023 2nd IACM Mechanistic Machine Learning & Digital Twins for Computational Science, Eng. & Technology
Venue:El Paso, TX, USA
Contact:
https://www.utep.edu/engineering/mmlde/index.html
2 - 4 Oct 2023 Structural Membranes 2023 – X! Int Conference on Textile Composites & Inflatable Structures
Venue:
Valencia, Spain
Contact:
https://structuralmembranes2023.cimne.com/
9 - 11 Oct 2023 PARTICLES 2023 - VII International Conference on Particle-based Methods
Venue:
Milano, Italy
Contact:
https://congress.cimne.com/particles2023/
10 - 13 Oct 2023 CMCS 2023 - II Int Conf on Computational Modeling of Complex Materials Across the Scales
Venue:
Eindhoven, Netherlands
Contact:
https://www.eccomas.org/conferences/
21 - 26 July 2024 WCCM/PANACM - 16th World Congress on Comp Mechanics & 4th PANACM Congress
Venue:
Vancouver, Canada
Contact:
https://www.cacse-accsg.ca/
34