Computational Fluid Dynamics
at the Nanoscale
Narayana R. Aluru
Simulation in Natural Hazards
Engineering and the
SimCenterFrank McKenna
Matt Schoettler
Greg Deierlein
Sanjay Govindjee
Parametrically-Upscaled
Constitutive Models(PUCM) with Uncertainty
Quantification for
Multiscale Modeling ofMetals and Composites
Somnath Ghosh
Practical Optimization
Philip E. Gill
Walter Murray
Margaret H. WrightBook Review
by Dan Givoli

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IACM Expressions
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2 Computational Fluid Dynamics at the Nanoscal
Narayana R. Aluru
8 Simulation in Natural Hazards Engineering and the SimCenterFrank McKenna, Matt Schoettler, Greg Deierlein, & Sanjay Govindjee
14 Parametrically-Upscaled Constitutive Models (PUCM) with Uncertainty Quantification for
Multiscale Modeling of Metals and Composites
Somnath Ghosh
21 Practical Optimization - Philip E. Gill, Walter Murray & Margaret H. WrightBook Review by Dan Givoli
23 IACMM Israel

contents

24 UKACM United Kingdom
26 USACM USA
28 GACM

Germany

30 SEMNI

Spain

32 JACMJapan

34 JSCESJapan

36 GIMCItaly

37 CEACM

Central Europe

38 CSMAFrance40 AMCA

Argentina

41 Conference Diary PlannerC omputational F luid D ynamics
at the N anoscale
by
N. R. Aluru
aluru@utexas.edu
The University ofTexas at Austin
USA

ntroduction

I

Computational fluid dynamics (CFD) has
played a central role in enabling many
important scientific and societal advancessuch as design of aircraft, understanding
blood flow through arteries, electronics cooling for chip design, and simulation of complex manufacturing processes for productinnovation. Classical CFD tools are built bysolving the compressible and incompressible Navier-Stokes equations using a variety
of sophisticated computational approaches.
Often, depending on the application, theNavier-Stokes equations are combined with
Maxwell’s equations and their simpler forms
to understand charged fluid transport. While
compressible and incompressible NavierStokes equations are accurate for simulationof physical phenomena at larger length andtime scales, an interesting question arises
on the accuracy of these equations at thenanoscale. During the last couple ofdecades, fluid mechanics at the nanoscale(also referred to as nanofluidics) has gained
a lot of prominence because of important
applications such as clean water, clean
energy, separations, nanomanufacturing,and selective transport of ions with important implications in disease diagnosis anddrug discovery. As the critical dimension

Figure 1:
Multiscale framework for nanoscale computational fluid dynamics(CFD). Structure (e.g. density profiles), dynamics (e.g., diffusion)and transport (e.g. velocity) – the essential components of nanoscale
CFD are shown in cyan color boxes.
The quasi-continuum framework can be combined with theclassical density functional theory to compute the thermodynamic
properties of fluids confined in nanometer channels and poresencountered in nanofluidics ranges from a
few Angstroms to several tens of nanometers, important deviations from classicalhydrodynamics are observed [1]. For
instance, density fluctuations near theinterface, size dependent fluid properties,
restricted translational and rotationalmotions of molecules, charge inversion
where the net charge density at an interface
is opposite in sign to the prediction fromclassical theory, flow reversal where thefluid moves in the opposite direction as thatpredicted from the classical theory, breakdown of classical constitutive relations, andseveral other important phenomena havebeen observed at the nanometer scale.
As a result, the use of classical CFD tools
to simulate fluid physics at the nanometerscale has been questionable.
To overcome the limitations of the classicaltheories for nanoscale fluid transport, quantum and atomistic approaches are widelyused. However, these approaches arelimited to small length and short time scales,
and it makes it difficult to use them in adesign environment for applications
involving nanoscale fluid transport. An
alternative approach is to develop multiscale
approaches seamlessly incorporating
atomistic physics into continuum theories
to enable rapid simulation of nanofluidic
systems. Over the last 1-2 decades,
efficient multiscale approaches havebeen developed to accurately simulatehydrodynamics at the nanoscale. These
multiscale simulation tools provide a robust
computational fluid dynamics environment
for nanoscale fluids. The rest of this article
describes a multiscale framework that
accurately describes fluid physics at thenanoscale.
II - Multiscale Framework
IACM Expressions readers will note that
authors are requested to submit articles withno equations. With this in mind, the basic
ideas in multiscale modeling of nanofluidics
are introduced using pictures and themathematical details can be found in thereferences. Simulation of nanoscale fluid
transport, including coupling to electric fields
as driving forces, can be essentially reduced
to computing the structure of the fluid (thefocus here is primarily on density, but otherstructural aspects of the fluid are also

iacm expressions 49/21 2

rU

important at nanoscale), dynamics (forsimplicity, the discussion here will focus on
diffusion, but other dynamical properties arealso of interest) and transport, primarilyreferring to the velocity of the fluid.
The multiscale framework to compute thestructure, dynamics, and transport ofnanofluidics is shown in Figure 1. To predict
the structure of the fluid, the Nernst-Planck
equation [2, 3] is used and the potential
energy term in the Nernst-Planck equationis computed by using the atomistic potentials describing the atomic interactions in thefluid. The interatomic potentials and thecoarse-grained potentials (as described indetail in Section III) can be obtained fromquantum-mechanical calculations but thisarticle will not delve into those details considering space limitations. Calculation of
the structure is described in more detail in
Section III. The solution of the NernstPlanck equation provides the density profile
as well as the potential of mean force (PMF)
which serve as input to the generalized
Langevin equation (for dynamics) andNavier-Stokes equations (for transport).
The generalized Langevin equation is used
to compute the trajectory of the fluid
molecules using which the diffusion coefficient as well the slip velocity of the fluid canbe computed.
The calculation of dynamical properties is
described in Section IV. The multiscale
framework described here can also be used
to compute thermodynamic properties andthis is also concisely summarized in Section
IV. Using inputs from the Nernst-Planck andgeneralized Langevin equations, the NavierStokes equations are solved to compute thetransport properties of the fluid. This is
described in more detail in Section V.

Figure 2:(top left) Quasi-continuum framework
to compute the structure of fluids;(top middle) The fluid-fluid potentials are approximated by asoft-core (top) and hard repulsion (bottom);(top right) Complex fluids (e.g., water) are represented bycoarse-grained beads(bottom left) comparison of density of argon from molecular
dynamics (circles) and quasi-continuum theory (solid line)(bottom middle) comparison of density of water from molecular
dynamics (circles) and quasi-continuum theory (solid line)(bottom right) comparison of density of ions (sodium andchloride ions) from molecular dynamics (circles) andquasi-continuum theory (solid line)
III - Structure
When a fluid is confined in a nanometerscale channel or pore, density inhomogeneity (layering of the fluid near the wall) isobserved. The density oscillations, whicharise due to the molecular interactions
between the fluid with itself and with thewall, cannot be captured using classicaltheories. In addition, as shown in Figure 1,
the density of a confined fluid is importantto also understand dynamics and transportof the fluid. Instead of resorting to more
expensive atomistic approaches, a quasicontinuum approach, where the interatomic
potentials are directly integrated into the
Nernst-Planck theory, can be used tocompute the structure. The basic idea isshown in Figure 2. While quasi-continuum
theories are prevalent in solid mechanics[4], for fluid mechanics it was first introduced
in [5]. The potential energy term in the
Nernst-Planck equation is computed using
the atomistic interatomic potentials which
are used to describe the fluid and wall inmolecular dynamics – exceptions to this are
discussed below. The various interactions
3 iacm expressions 49/21

that contribute to the potential energy canbe a function of the fluid density, so a simple
iterative scheme is shown in Figure 2 toself-consistently solve for the fluid density
and the components of the potential energy.
It is important to note that the potential
energy dependence on density is highly
nonlinear and good initial guesses (e.g.,based on the Boltzmann distribution [6]) andeffective nonlinear solvers are required toaccurately compute the density profile.

“ The multiscale
methods describedhere to computethe structure,dynamics,
thermodynamics,
and transport offluids confinedin nanometerchannels andpores are apoint ofdeparture fromthe classical CFD
tools that are
widely used at
macroscale..”iacm expressions 49/21 4

Consider, first, simple fluids, which canbe described by single-site isotropic pairpotentials in atomistic simulations. In thiscase, van der Waals interactions [7]between fluid atoms (referred to as fluidfluid interaction energy) and between thefluid and the wall (referred to as wall-fluid
interaction energy) need to be considered.
For most walls of interest, the wall-fluid
interaction energy can be computed analytically by invoking the density of wall atoms(see [8] for more details). By invoking thedensity of a fluid, the fluid-fluid interactionenergy can be computed by using the interatomic potential. However, the repulsive
nature of the potential for short distances
poses numerical difficulties. To overcome
this, a mean-field approach, where a softcore potential is used (see Figure 2 (topmiddle)) to approximate the repulsive part ofthe interatomic potential, a correlation-correction potential approach [9] where thepair correlation function is included in theformalism to overcome the mean-fieldapproximation, and a fundamental measure
theory where the repulsive part of thepotential is approximated by a hard-spheremodel (see Figure 2 (top middle)) aredeveloped [10]. The unknown parametersin these approaches are determined bymatching the potential of mean force (PMF)[11]. While all three approaches provide
good accuracy, the fundamental measure
theory-based approach is predictive and canbe used for a variety of fluids with minimal
effort (see Figure 2 (bottom left) for a typicalresult comparing molecular dynamics andquasi-continuum theory).
The quasi-continuum approach has also
been extended to fluids with many-bodyinteractions. For example, the charges on
the oxygen and hydrogen atoms of water
introduce electrostatic interactions which
are long ranged. Since water molecules are
represented by a density in the quasicontinuum approach, the charge information
is not readily available. To address thischallenge, each water is coarse-grained intoa single spherical bead (see Figure 2 (topright)) and the interatomic potentials

describing water-water and water-wall are
obtained by preserving some basic properties of the original all atom system. Somewidely used approaches to develop coarsegrained (CG) potential for water and othercomplex fluids include iterative Boltzmann
inversion (IBI) [12] – which preserves thebulk radial distribution function, force
matching [13] – which preserves the forces
between the all atom and CG systems, andrelative entropy [14, 15] – which minimizes
the information lost between all atom andCG systems. The CG potentials for water
can be either directly incorporated or furthercoarse-grained before incorporation in thequasi-continuum framework. Figure 2(bottom center) shows a typical result
where the structure of water is compared
between molecular dynamics and thequasi-continuum theory. The quasicontinuum framework can be extended tomore complex fluids such as electrolytes,
DNA, polymers, etc. For example, quasicontinuum treatment of electrolytes has
been shown to effectively capture thecharge inversion phenomenon (see [16])which is missed by the classical theory.
IV - Dynamics, Thermodynamics
The dynamical properties of fluids(e.g., diffusion coefficient) at nanoscale
can be different from their bulk properties.
A straight-forward approach is to use molecular dynamics simulations. In molecular
dynamics, the diffusion coefficient for thebulk fluid is usually determined by computing the mean-squared displacement (MSD)of the molecules, ⟨Δr(t)2⟩, as a function oftime and fitting this behavior to the asymptotic time dependence predicted from thediffusion equation. For confined fluids, thediffusion coefficient can be anisotropic, i.e.,the diffusion coefficient can be different
along x-, y-, and z-directions. Furthermore,
for inhomogeneous systems, where density
layering is significant, molecules stay in theregion of interest only for a finite time andthen explore other regions. Thus, diffusion
coefficient is expected to be different fordifferent regions (spatial dependence ofdiffusion coefficient). Even though molecular dynamics simulations can be used tounderstand diffusion physics, they are
expensive and limited to small systems.
The quasi-continuum framework can
be combined with the memory function
equation and generalized Langevin equation(GLE) (see Figure 3 top) to computeanisotropic and spatial variation of the
diffusion coefficient [17]. In this multiscale
approach, the density profile and thepotential of mean force (PMF) are first computed by using the quasi-continuum theory.
In a separate step, the memory function
equation (see Figure 3 top), which is an
integro-differential equation, is solved todetermine the memory kernel. The dynamical variable (Ψ) describes normalized velocity autocorrelation function of a bulk fluidwhich is typically known, e.g., this can beobtained from molecular dynamics or experiments or via stochastic extension of thequasi-continuum theory. Once the PMF andthe memory kernel are known, GLE can besolved to generate dynamic equilibrium trajectories of the fluid molecules. The generated trajectories can be used to computequantities such as MSD and survival probabilities which can be used to estimate thediffusion coefficient. It is important to notethat the solution of the memory function
equation/GLE equation, with PMF obtained
from the quasi-continuum theory, to obtain
the diffusion coefficient is many orders ofmagnitude faster compared to the use ofmolecular dynamics. This is because thetime step used in the generalized Langevinequation is much higher compared to that of
the time step used in MD simulations.
A significant advantage of using the quasicontinuum/GLE approach, in addition to thefast calculation of the diffusion coefficient, isto estimate fluid-solid interaction properties
such as the friction factor which can beused to calculate the slip velocity of a fluidon a solid wall. The friction factor, derived
using linear response theory [18], is shownin Figure 3 (middle). The expression for thefriction requires the calculation of force autocorrelation and force-velocity cross correlation functions. Since forces and velocities
as a function of time are obtained from thesolution of GLE, the friction factor can be
estimated easily. Figure 3 (bottom) showsforce autocorrelation and force-velocity
cross correlations for interaction of waterwith a silicon surface. The results from GLE
compare well with those from MD. It isagain important to note that the estimationof the friction factor using the GLE approachis orders of magnitude faster compared tousing the molecular dynamics framework.

Figure 3:(top left) Memory function equation to determine
the memory kernel (Kq);(top right) Generalized Langevin equation (GLE)
describing the dynamics of particles;(middle left) Mathematical expression to determine
the friction (and subsequently the slip of the fluid) that relies on
force autocorrelation and force-velocity cross-correlation;(middle right) Definition of grand potential in classical density
functional theory (cDFT); the excess free energy is computedby using the quasi-continuum potentials;(bottom left) Comparison of force autocorrelationfrom GLE and molecular dynamics (MD);(bottom right) Comparison of force-velocity
cross correlation from GLE and MD

determine the grand potential in termsof the equilibrium density of the fluid (seeFigure 3 middle). The excess part of the
intrinsic Helmholtz energy accounting fornon-bonded interactions is often difficult tocompute, but quasi-continuum potentials
can be used to accurately determine theexcess part of the Helmholtz free energy.
Thus, by combining the quasi-continuum
framework with cDFT, thermodynamic
properties such as adsorption, local
pressure tensor, surface tension, andsolvation force, of confined fluids can becomputed (see [9] for more details).
V - Transport

As shown in Figure 1, the multiscale
framework can also be used to compute thethermodynamic properties of confined fluids.
For fluids confined in nanometer channels
and pores, the thermodynamic relationshipbetween the grand potential and otherthermodynamic properties such as entropy,
temperature, pressure, etc. can be easily
established. The classical density functional
theory (cDFT) [19] provides a framework to

The governing equations for nanoscaletransport are the Navier-Stokes equations(see Figure 4 (top)). The classical NavierStokes equations, however, do not always
accurately describe transport at thenanoscale because of complex interfacial
physics – for example, two well known
effects are the slip velocity and interfacial
viscosity. Fluid-surface interactions and the
5 iacm expressions 49/21

and described in the previous section, theslip velocity is computed using the generalized Langevin equation. The approach tocompute the spatially varying viscosity issummarized in Figure 4 (middle). Using
the density from the quasi-continuum theory,
a local averaged density is first computed.
The local averaged density is then mapped
to a bulk homogeneous system and theviscosity of the corresponding bulk system
is computed. The bulk viscosity, corresponding to the local averaged density,
is then used as the local viscosity of the

Figure 4:(top left) Navier-Stokes equations along with the slip boundary
conditions used for nanoscale transport;(top right) Viscosity of water as a function of density(middle) Approach used to determine viscosity of confined fluids(bottom left) transport of water in graphene channels – solid line ismultiscale simulation and error bars denote MD data(bottom middle) transport of water in a silicon channel(bottom right) electroosmosis of water; charge inversion causes
flow reversal and multiscale theory accurately describes flow reversal
wall topology give rise to surface friction,
which affects the collective motion of thefluid particles relative to the wall. The surface friction changes due to the potential
corrugations, which changes due to the
topology of the surface and the interaction
potential between the fluid and the surface.
It is important to account for surface friction
effects which appear in the form of slip
velocity in the transport model. It is also
important to know where the slip velocity
should be applied. Since there are essentially no fluid molecules at the wall, the slip
velocity is applied at a distance from thewall where the density of the fluid is finite.
This information is available from thequasi-continuum calculation of density
profiles. The second aspect that is importantis the viscous component of the flow. In
macroscopic channels, viscosity is assumed
to be constant in the entire channel.
However, viscosity in confined channels
can be different. Density layering near thesurface introduces viscosity variation. As
a result, viscosity can vary spatially in the
nanochannel and it is important to accountfor this phenomenon. As shown in Figure 1
iacm expressions 49/21 6

confined channel. This assumption works
reasonably well for most nanochannels ofinterest. The bulk viscosity of most fluids
as a function of density is known. In theabsence of such relations, molecular
dynamics can be used to compute densityviscosity relations. Shown in Figure 4 (topright) is the density-viscosity relation forwater. Once the various inputs to theNavier-Stokes equations are known (seeFigure 1), the Navier-Stokes equations canbe solved using well established methods
to determine the velocity of the fluid.
The multiscale method described above
has been used to investigate transport ofa variety of fluids confined in nanometerchannels [20]. Some representative results
are shown in Figure 4 (bottom). Figure 4(bottom left) shows the transport of waterin a graphene channel. A significant slip
velocity is observed in this system. The
multiscale result (solid line) matches wellwith the molecular dynamics result (errorbars). Figure 4 (bottom middle) showstransport of water in a silicon channel
where the slip velocity is not significant andviscous nature of the flow is observed in thecentral part of the channels. Again, a goodcomparison between multiscale method andmolecular dynamics is observed. Figure 4(bottom right) shows an example wherewater and electrolytes are confined in a
nanochannel. This is an example wherecharge inversion is observed, and theclassical Navier-Stokes theory predicts thatthe water velocity is positive while moleculardynamics predicts that the water velocity is
negative. Multiscale theory, with inputs
from the quasi-continuum theory for thedensity and the location of the slip velocity,
the generalized Langevin theory for theslip velocity, and the local density approximation for the local viscosity, predicts thatthe water velocity is negative and the result
matches reasonably well with the molecular
dynamics result.
VI - Concluding Remarks
The multiscale methods described here tocompute the structure, dynamics, thermodynamics, and transport of fluids confined in
nanometer channels and pores are a pointof departure from the classical CFD toolsthat are widely used at macroscale. Many
important practical applications involve
coupling of macroscale fluid mechanics
with nanoscale fluid mechanics and efficientsimulation of these problems is now possible via the multiscale methods described
here. A number of issues such as thedevelopment of coarse-grained potentials
that are reliable at various thermodynamic
states, seamless integration of structure,dynamics, thermodynamics and transport
multiscale tools into one coupled computational environment, developing constitutive
theories that are accurate and reliable atpurely molecular limit, extraction of important transport parameters and properties
such as slip and viscosity without resorting
to coarse-grained theories are among themany challenges that will further push thefrontiers on the development of multiscale
methods for nanoscale hydrodynamics. l

Acknowledgments
The author would like to thank several students and postdocs who
have contributed to the development of multiscale methods
described here. In particular, the author is indebted to R. Bhadauria,
S. Mashayak, M. H. Motevaselian, J.-H. Park, A. Paul,
A. Raghunathan, T. Sanghi, and H. Wu. The author would also liketo acknowledge funding support from the National Science
Foundation, DARPA, NIH, AFOSR, ONR, and DOE.

References
1. S. Faucher, N. Aluru, M.Z. Bazant, D. Blankschtein, A.H. Brozena, J. Cumings, J.P. de Souza, M. Elimelech,
R. Epsztein, J.T. Fourkas, A.G. Rajan, H.J. Kulik, A. Levy, A. Majumdar, C. Martin, M. McEldrew, R.P. Misra,
A. Noy, T.A. Pham, M. Reed, E. Schwegler, Z. Siwy, Y.H. Wang, & M. Strano,
Critical Knowledge Gaps in Mass Transport through Single-Digit Nanopores: A Review and Perspective.
Journal of Physical Chemistry C, 2019. 123(35): p. 21309-21326, DOI: 10.1021/acs.jpcc.9b02178.
2. W. Nernst, Zur Kinetik Der in Lösung Befindlichen Körper. Zeitschrift für physikalische Chemie, 1888. 2(1): p. 613-637.
3. M. Planck, Ueber Die Erregung Von Electricität Und Wärme in Electrolyten. Annalen der Physik, 1890. 275(2): p. 161-186.
4. E.B. Tadmor, M. Ortiz, and R. Phillips, Quasicontinuum Analysis of Defects in Solids. Philosophical magazine A,
1996. 73(6): p. 1529-1563.
5. A.V. Raghunathan, J.H. Park, and N.R. Aluru, Interatomic Potential-Based Semiclassical Theory for Lennard-Jones
Fluids. Journal of Chemical Physics, 2007. 127(17), DOI: 10.1063/1.2793070.
6. L. Boltzmann, Weitere Studien Über Das Wärmegleichgewicht Unter Gasmolekülken. Sitzungsberichte Der Akademie
Der Wissenschatften. Wien Ii, 66: 275. English Translation in S G Brush (1966) Kinetic Theory,
Vol. 2 Irreversible Processes: 88-175. 1872, Pergamon press, Oxford.
7. J.E. Lennard--Jones, The Electronic Structure of Some Diatomic Molecules.
Transactions of the Faraday Society, 1929. 25: p. 0668-0685, DOI: 10.1039/tf9292500668.
8. T. Sanghi & N.R. Aluru, A Transferable Coarse-Grained Potential to Study the Structure of Confined, Supercritical
Lennard-Jones Fluids. Journal of Chemical Physics, 2010. 132(4), DOI: 10.1063/1.3289722.
9. S.Y. Mashayak, M.H. Motevaselian, & N.R. Aluru, An Eqt-Cdft Approach to Determine Thermodynamic Properties ofConfined Fluids. Journal of Chemical Physics, 2015. 142(24), DOI: 10.1063/1.4922956.
10. S.Y. Mashayak & N.R. Aluru, Langevin-Poisson-Eqt: A Dipolar Solvent Based Quasi-Continuum Approach forElectric Double Layers. Journal of Chemical Physics, 2017. 146(4).
11. T. Sanghi & N.R. Aluru, Coarse-Grained Potential Models for Structural Prediction of Carbon Dioxide (Co2) in
Confined Environments. Journal of Chemical Physics, 2012. 136(2), DOI: 10.1063/1.3674979.
12. H. Wang, C. Junghans, and K. Kremer, Comparative Atomistic and Coarse-Grained Study of Water: What Do We Lose
by Coarse-Graining? European Physical Journal E, 2009. 28(2): p. 221-229, DOI: 10.1140/epje/i2008-10413-5.
13. S. Izvekov & G.A. Voth, Multiscale Coarse Graining of Liquid-State Systems.
Journal of Chemical Physics, 005. 123(13), DOI: 10.1063/1.2038787.
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Journal of Chemical Physics, 2008. 129(14), DOI: 10.1063/1.2992060.
16. S. Mashayak and N. Aluru, A Multiscale Model for Charge Inversion in Electric Double Layers.
The Journal of chemical physics, 2018. 148(21): p. 214102.
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Dynamics of Confined Fluids. Journal of Chemical Physics, 2013. 138(12), DOI: 10.1063/1.4796387.
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7 iacm expressions 49/21

S imulation in N atural H azards E ngineeringand the S im C enter
byFrank McKenna1,
Matt Schoettler1,
Greg Deierlein2, &Sanjay Govindjee1
1
UC Berkeley,
Dept of Civil &Environmental
Engineering2
Stanford University,
Dept of Civil &Environmental
Engineering
s_g@berkeley.edu

ntroduction

I

Much of the world’s population lives inregions susceptible to earthquakes,
tropical cyclones (hurricanes), or othernatural hazards, where the risks areexacerbated by aging buildings and civil
infrastructure that often are not designed
to resist the hazards. While knowledge
and data gained through field observations
and experiments are fundamental toaddressing challenges associated withplanning and design of resilient communities, computational simulations are seen
as an essential component of the scienceand engineering needed to evaluate andmitigate the potential devastating effects
of natural hazards, some of which areintensifying in magnitude and/or frequency
due to climate change; further, risk
exposure to all hazards is increasing
due to population growth and urbanization(NHERI 2020).
To date, the primary focus of computational
simulations with regards to natural hazards
engineering (NHE) has been on applications for isolated purposes. Examples
include:“ Our goal is tosupport researchthat will enabledependable
regional resilienceassessments ...
that reduce
damage ...
after an event,& ...improve
community
resilience.”

1. Hazard simulation (EVENT):
This refers to direct modeling of thephysics of a hazard, such as seismic
wave propagation models for strong
ground shaking, shallow-water solvers
for coastal inundation, or Weather
Research and Forecasting type models
wrapped around Large Eddy
Simulations for the multiscaleassessments of wind flows.
2. Asset response simulation (FEM):
This typically involves simulating the
response (displacements, accelerations,stresses, etc.) of an individual structure(building, bridge, utility pipeline, etc.)
using the finite element method todetermine the demand parameters fora given hazard EVENT.
3. Damage and losses estimation (DL):This refers to predicting the resulting
monetary losses and downtime forrepairs given a FEM prediction of
structural response of buildings orother infrastructure.

iacm expressions 49/21 8

4. Community recovery (REC):
This refers to predicting population
displacement, evacuation scenarios,
and regional recovery for a given
EVENT and subsequent DL.
The effects of natural hazards at a regional
scale can be evaluated by simulating
the hazard event, performing detailed
analyses of localized response and damageof individual buildings and infrastructurecomponents, and integrating the results
through multi-scale analyses of regionally
distributed communities and infrastructure
systems. The manual effort needed toperform a comprehensive regional
analysis, utilizing existing applications
that individually solve just one step of the
EVENT-FEM-DL-REC workflow, is time
consuming, cumbersome, and error prone.
Furthermore, such work requires expertisewith the various applications. The consequence is that researchers have generally
maintained a narrow focus on certain
aspects of the problem. For example,
researchers involved with earthquake
simulation usually limit their investigations
to predicting the maximum accelerations ina region, without going further to simulate
the damaging effects of the motions.
Moreover, there is limited collaborative
work across the many disciplines that makeup natural hazard engineering; and there islittle connection between NHE researchers
and those in urban planning and the social
sciences. The result is a state of the artthat is overly simplistic, and which misses
the opportunities for comprehensive efforts
to improve community resilience.
The Natural Hazards Engineering Research
Infrastructure’s Computational Modelingand Simulation Center (SimCenter) is a
National Science Foundation (NSF) facility
funded to provide open-source softwarethat seeks to overcome this situation
through the development of an application
framework for creating targeted scientificworkflow systems for NHE researchers.
Scientific workflow systems are applications
that allows users to create, launch, andmonitor scientific workflows (i.e., in-silico
workflows). In the SimCenter systems,
the user defines the workflow in a desktop
application and a backend application
manages the running workflow by bothlaunching the different applications in theworkflow and handling the flow of data
between the applications.
While several general-purpose workflow
systems exist, e.g., Taverna (Oinn et al.,2003), Kepler (Altintas et al., 2004),
Pegasus (Deelman et al., 2015), etc., theSimCenter is developing domain specificsystems for NHE with a focus on defining
APIs for the different components of theworkflow. For example, all EVENT
applications accept a standardized JSON
input and produce a standardized JSON
output, permitting users to choose from avariety of EVENT applications provided bydifferent researchers. The use of JSON
allows applications to handle the standard
API data requirements and to access
additional application specific data. To
accommodate the inclusion of new
applications into the workflows, theSimCenter publishes APIs for application
developers and writes pre- and postprocessors for common applications as
examples for developers. In addition, theSimCenter workflow systems provide
simple graphical user interfaces (GUIs)
that researchers can use to harness
DesignSafe-ci provided HPC resourcesat the NSF funded Texas Advanced
Computing Center (TACC) and to facilitate
the use of Uncertainty Quantification(UQ) in a rigorous manner. As a SimCenter
priority, UQ is automatically included inits scientific workflows, thus facilitating
the characterization and propagation of
uncertainties throughout the workflow.
SimCenter application framework
The application framework provided by
the SimCenter, cf. Figure 2, consists ofa collection of components (shaded
rectangles) plus workflow systems(unshaded rectangles) that are built using
these components, and several pre- andpost-processors in the shaded SimCeA
box, that invoke existing applications
and interface with external web services.

Figure 1:
Modular SimCenter framework

Several of these components represent
the four application types identified in the
introduction: EVENT, FEM, DL, and REC.
In addition to these four, the frameworkprovides other components:
UQ: The UQ component provides aninterface to software and routines formethods of uncertainty quantification(sampling, reliability, sensitivity),
optimization and surrogate generation.
Libraries being used include Dakota(Adams et al., 2014) and UQpy (Olivier
et al., 2020), in addition to the SimCenter’s
own UQ engine.
CLOUD: A component that interacts withcloud-based resources. The current cloudcomponent uses Tapis (Agave), a TACC
Cloud API. The CLOUD API is designed
such that other cloud components areeasily integrated, allowing runs at any ofthe large cloud providers (e.g., Amazon
Web Services, Google Cloud Platform,
IBM Azure, etc.).
SAM: Structural Analysis Modeling (SAM)
is the workflow component that includes
rule-based, artificial intelligence (AI), andother types of applications to translate
descriptive information about the built
environment (e.g., building inventory) into
information to create finite element orother types of models to simulate structural
response to hazards.
BRAILS: Building Recognition using AI at
Large Scale (BRAILS, Wang et al. 2019)is a SimCenter application dedicated tousing machine learning (ML) to generate
information about buildings in a region
from multiple data streams, including
imagery (satellite and street view images)
and textual data (tax assessor data).
9 iacm expressions 49/21

Figure 2:
SimCenter Framework Components and Applications
Central to NHE computations is a large
amount of data about the built environment.
Databases needed by the framework, theshaded cylinders in Figure 2, include:
BE – Built Environment Inventory: The
BE consists of meta-data and data
files that define the inventory of physicalassets for regional simulations, including
buildings, transportation components, utility
infrastructure, etc. The data for the BE
often comes from automated scraping of
public websites and other on-line datasets.
DL Data: Databases of (probabilistic)fragility curves for damage and loss
estimation for various types of assets
subjected to the demands from various
hazards (earthquake, wind, storm surge,etc.).
Exp/Sim Data: Databases of experimental
and/or computational research data that is
utilized for the machine learning SAM
applications and code validation.
Finally, the unshaded boxes in Figure 2
are desktop applications that pre-package
iacm expressions 49/21 10

common workflows built from SimCenter
framework components. These desktop
applications facilitate launching workflows
locally or on HPC resources as shown in
Figure 3.
These applications include:quoFEM: The Quantified Uncertainty
with Optimization for the Finite Element
Method application facilitates uncertainty
quantification calculations by combining
software systems for uncertainty
quantification and optimization with FEA(and other) analysis codes. As shown in
Figure 2, quoFEM is built upon the UQ
and CLOUD resources of the framework.
EE-UQ, WE-UQ, and Hydro-UQ: These
applications allow users to perform
sampling, reliability, and sensitivity analyses
for buildings subjected to input groundmotions, wind flows, and hydrodynamic
forces due to earthquake, wind, andstorm surge/tsunami loading events. The
tools play an important role under-pinning
regional simulations, as reliable models of
single structures are an integral part ofhigh-resolution regional simulations.
Research is crucially needed to improvewind and hydrodynamic simulation

capabilities, as at present they cannot
reliably evaluate buildings and otherstructures subjected to turbulent wind
and water flows.
PBE: The performance-based engineering
application is an extensible workflow
application to evaluate the damage, losses,
and downtime of buildings or other assets
to natural hazard events.
R2D: The Regional Resilience
Determination Tool facilitates regional
hazard scenario studies. The tool
presently determines damage and loss
for a region’s building inventory for
hurricane and earthquake events.
The application is being expanded toinclude REC.

Workflow example with R2D
The R2D tool is the primary SimCenter
application for regional scale simulations.
Figure 4 shows a comparison of results
generated for a magnitude 7.0 earthquake
impacting the San Francisco Bay area
using R2D versus results from a large study
organized by the US Geographical Survey(USGS). On the left side of Figure 4 areresults for an R2D simulation that utilized
an SW4 (Petersson and Sjogreen, 2017)
rupture simulation (EVENT) to individually
shake 1.84 million buildings obtained by
combining an UrbanSim (Waddell, 2002)
database with Microsoft footprint data
(Microsoft, 2020). Each building wasmodelled as a nonlinear shear building
using MDOF-LU (Lu et al., 2014) withrandom variables assumed for structural
stiffness, mass, and story yield properties.

Figure 3:
Integration of the
SimCenter Framework
into the NHERIComputational
Environment
11 iacm expressions 49/21

For a few dozen realizations, PELICUN(Zsarnóczay and Deierlein, 2020) was used
to perform HAZUS-like (Schneider and
Schauer, 2006; Kircher et al., 2006, Vickery
et al, 2006) damage and loss estimates.
The color shading represents the loss ratios
for each building, calculated as the mean
repair costs normalized by the building
replacement value. On the right side of
Figure 4 are the loss ratios reported in the
USGS Mw 7.0 Haywired Earthquake
Scenario (Hudnut et al, 2018) which
represents results from a state-of-the-art
risk analysis. Exposure and losses inthe Haywired scenario were calculatedusing the HAZUS software. While it isinstructive to compare results between
the two studies, there are differences in
the input data, scope, and goals of thestudies which are important to keep in
Figure 4:
Comparison of Building Loss Ratios from San Francisco
Testbed – SimCenter (left), USGS-Haywired (right).

iacm expressions 49/21 12

mind. The models and results in the
SimCenter study are preliminary, basedon readily available information, andimplemented by a small team over a
couple months as an exercise to test R2D
and its backend rWHALE. This contrasts
with the multi-year multi-investigator
Haywired study, whose goal was toinform earthquake planning and preparedness for the San Francisco Bay Area.
An important distinction between the
HAZUS-based Haywired study and the
SimCenter workflow simulation is thelevel of resolution in the assessment andthe propagation of various sources of
uncertainty throughout the simulation.
Whereas the HAZUS-based study
aggregates building damage and losses
based on census track (postal code) data,
the SimCenter workflow has resolution
down to the building parcel level and candisaggregate losses within a building down
to individual components on each floor(when such information is desired).

Concluding Remarks
The SimCenter provides an application
framework for developing scientificworkflow systems for NHE engineeringthat inherently take advantage of HPC
and UQ. The SimCenter has released
several applications using this framework,
which presently aid NHE researchers tounderstand the effects and risks ofnatural hazards on the built environment.
Our goal is to support research that will
enable dependable regional resilienceassessments to inform mitigation
efforts that reduce damage to homes
and businesses after an event, andthereby, improve community resilience.

Given the state of practice, contributions
from researchers across disciplines
are needed to realize this goal, and thepresented open-source frameworkprovides a common platform to mobilize
these efforts. l
Acknowledgements
The SimCenter is financially supported by the National Science
Foundation under Grants CMMI 1612843 and 2131111. Any opinions, findings, and conclusions or recommendations expressed inthis material are those of the authors and do not necessarily reflect
the views of the National Science Foundation. FM, MS, GD, SG
further acknowledge the combined efforts of the entire SimCenter
team; see https://simcenter.designsafe-ci.org/.

References
[1]

[2]

[3]

[4]

[5]
[6]

[7]
[8]
[9]

[10]

[11]
[12]
[13]
[14]
[15]
[16]

Adams, B.M., Bohnhoff, W.J., Dalbey, K.R., Ebeida, M.S., Eddy, J.P., Eldred, M.S., Geraci, G., Hooper,
R.W., Hough, P.D., Hu, K.T., Jakeman, J.D., Khalil, M., Maupin, K.A., Monschke, J.A., Ridgway, E.M., Rushdi,
A.A., Stephens, J.A., Swiler, L.P., Vigil, D.M., Wildey, T.M., and Winokur, J.G. (2019), Dakota,
A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation,
Uncertainty Quantification, and Sensitivity Analysis: Version 6.11 User’s Manual,
Sandia Technical Report SAND2014-4633, July 2014; updated November 2019.
Altintas I, Berkley C, Jaeger E, et al. (2004) Kepler: an extensible system for design and execution ofscientific workflows. In: 16th international conference on scientific and statistical database management,
Santorini Island, Greece, 21–23 June 2004.
Deelman, E., Vahi, K. Juve, G., Rynge, M., Callaghan, S., Maechling, P.J., Mayani, R., Chen, W.,
Ferreira da Silva, R., Livny, M., and Wenger, K. (2015) Pegasus: a Workflow Management System for
Science Automation, Future Generation Computer Systems, 46, 17–35.
Hudnut, K.W., Wein, A.M., Cox, D.A., Porter, K.A., Johnson, L.A., Perry, S.C., Bruce, J.L, LaPointe, D. (2018),
The HayWired earthquake scenario – We can outsmart disaster, USGS, Fact Sheet 2018-3016,
https://doi.org/10.3133/fs20183016
Kircher, C.A., Whitman, R.V., Holmes, W.T. (2006), HAZUS Earthquake Loss Estimation Methods,
Hatural Hazards Review, 7(2), 45-59.
Lu, X., Han, B., Hori, M., Xiong, C. and Xu, Z. (2014) A coarse-grained parallel approach for seismic
damage simulations of urban areas based on refined models and GPU/CPU cooperative computing,
Advances in Engineering Software, V70, 90-103.
Microsoft (2020), Microsoft Building Footprint Database for the United States,
https://www.microsoft.com/en-us/maps/building-footprints
NHERI (2020), Natural Hazards Engineering Research Infrastructure – Five-Year Science Plan, MultiHazard Research to Make a More Resilient World, https://www.designsafe-ci.org/facilities/nco/science-plan/Oinn, T., Addis, M., Ferris, J., Marvin, D., Senger, M., Greenwood, R., Carver, K., Pocock, M.G., Wipat, A., Li, P.
(2003), Taverna: a tool for the composition and enactment of bioinformatics workflow.
Bioinformatics 20(1), 3045–3054.
Olivier, A., Giovanis, D.G., Aakash, B.S., Chauhan, M., Vandanapu, L., and Shields, M.D. (2020).
UQpy: A general purpose Python package and development environment for uncertainty quantification.
Journal of Computational Science.
Petersson, N.A.; Sjogreen, B. (2017), SW4, version 2.0 [software], Computational Infrastructure of
Geodynamics, doi: 10.5281/zenodo.1045297, url: https://doi.org/10.5281/zenodo.1045297
Schneider, P.J., Schauer, B.A. (2006), HAZUS – Its Development and Its Future,
Natural Hazards Review, ASCE, 7(2): 40-44.
Vickery, P., Lin, J., Skerlj, P., Twisdale, L., and Huang, K. (2006). HAZUS-MH Hurricane Model Methodology. I:
Hurricane Hazard, Terrain, and Wind Load Modeling. Natural Hazards Review, 7(2), 82-93
Waddell P. (2002) UrbanSim: Modeling Urban Development for Land Use, Transportation, andEnvironmental Planning, Journal of the American Planning Association, vol. 68, no. 3, pp. 297-314
Wang C., Yu Q., McKenna F., Cetiner B., Yu S.S., Taciroglu E., and Law K.H. (2019).
NHERI-SimCenter/BRAILS: v1.0.1 (Version v1.0.1). Zenodo. http://doi.org/10.5281/zenodo.3483208
Zsarnoczay, A., Deierlein, G.G. (2020), PELICUN – A Computational Framework for Estimating
Damage, Loss and Community Resilience, Proceedings, 17th World Conference on Earthquake Engineering,Sendai, Japan
13 iacm expressions 49/21

P arametrically- U pscaled C onstitutive M odels (PUCM)
with U ncertainty Q uantification for M ultiscale
M odeling of M etals and C omposites
bySomnath Ghosh
Departments ofCivil & Systems Eng.,
Mechanical Eng.
and Materials
Science & Eng.
Johns Hopkins Uni.,
Baltimore, USA
sghosh20@jhu.edu“ Explicit
dependence on the
microstructure,significant
computational
efficiency,
and ease of
integrating withcommercial
software makes
the PUCM
a veryimportant tool
for structural
analysis andmaterial design.”

iacm expressions 49/21 14

1

Introduction

Structural materials like metals andcomposites are typically characterized byheterogeneous microstructures containing
nonuniform distributions of grains, colonies,
fibers, particulates, and other features.
Morphological and crystallographic characteristics strongly govern their mechanicalbehavior and failure response. For example,
in polycrystalline alloys, variations in grainsize, crystallographic orientations, microtexture, and slip-system resistance lead tolarge gradients in stresses and grainboundary dislocations leading to fatigue
crack nucleation. Microstructure-based
mechanistic modeling of representative
volume elements (RVEs) using finite element(FE) or fast Fourier transform (FFT)-based
solvers is a commonly accepted practice foranalyzing the influence of microstructuraldescriptors on the overall material behavior.
Pure micromechanical analysis becomescomputationally prohibitive for structuralscale modeling, due to requirements ofrepresenting large degrees of freedom inthe underlying microstructure. Macroscopic
modeling with experimentally calibrated
phenomenological constitutive models, on
the other hand, have significant limitations
due to inadequate connection with thematerial microstructure and underlying
physics. This has prompted the development of multi-scale models for predicting
the upscaled response of heterogeneousmaterials. These methods include meanfield homogenization [1], asymptotic
homogenization methods [2,3,4], higherorder homogenization methods [5,6], FE2
multi-scale method [7], reduced-ordermodeling [8], data-driven homogenization[9], data-analytics based reduced-ordermodeling [10,11] and data-science based
approaches with machine learning [12,13].
There is a strong need for building accurate
and efficient, physics-based multiscalemodels representing microstructural featuresand underlying mechanisms of deformationand failure. Physics-based constitutive
relations, developed using data fromexperimentally-validated micromechanicalsimulations as upscaled transfer functions,
are necessary ingredients of this endeavor.

1.1 Parametric Upscaling: The concept ofparametric upscaling in constitutive models
has been introduced by the Ghosh group in[14-23] to: (i) bridge length-scales throughthe explicit representation of lower-scale(microstructural) descriptors in higher-scaleconstitutive models, and (ii) overcome highcomputational costs incurred by manyhomogenization methods through anefficient computational platform. The
physics-informed Parametrically-Upscaled
Constitutive Models or PUCMs differ fromconventional phenomenological models intheir unambiguous depiction of constitutive
parameters and their dependencies. The
thermodynamically-consistent PUCMs,
incorporate a parametric representation oflower-scale microstructural descriptors
in higher-scale constitutive coefficients.
These PUCM coefficients are expressed
as functions of Representative AggregatedMicrostructural Parameters or RAMPs,
representing lower-scale descriptors of
microstructural morphology and crystallography, e.g., texture and orientation distribution,
grain, and reinforcement size, etc. Following
the second law of thermodynamics, thegeneral forms of PUCM equations are
a-priori selected to reflect the fundamental
deformation characteristics of aggregatedmicromechanics response of microstructural
statistically equivalent representative volume
elements or (SERVEs). Such deformation
characteristics include objectivity, anisotropy,
tension-compression asymmetry, hardeningsoftening behavior etc. The first law ofthermodynamics, governing the mathematical theory of homogenization, is used tobridge length scales and express constitutive
coefficients as functions of lower-scale
RAMPs. Machine learning (ML) tools, e.g.,
symbolic regression and artificial neuralnetworks (ANN) are essential for generating
PUCM constitutive coefficients as functionsof the RAMPs, using data-sets generated
from micromechanical simulations. The
PUCM framework provides a window for
the ML tools to create meaningful physicsinformed constitutive coefficients as functions of lower-scale descriptors. Since thewindows are bound by thermodynamics,
the requirement of a very large database is
mitigated with this approach. The PUCMs
are readily incorporated in FE codes like

ABAQUS through user-defined material
modeling windows such as UMAT. A
significantly reduced number of solution
variables in the PUCM simulations,compared to micromechanical models, makethem several orders of magnitude more
efficient, but with comparable accuracy.

1.2 Steps in Developing the PUCMs:
The steps are delineated in Table 1.
• Module 1 acquires data frommicrostruc-tural characterization andmechanical testing for model calibrationand validation.
• Module II first establishes microstructurebased statistically equivalent RVEs or
SERVEs. Following this, image-based
micromechanical models are calibrated,and micromechanical simulations of the
SERVE are performed for validation withexperiments.
• Module III begins with the creation of adatabase of evolving state variables frommicromechanical simulations of the
SERVEs with various microstructure andload combinations. Subsequently, theRAMPs of morphological and crystallographic descriptors, such as grain size,
shape, orientation and misorientation
distributions, are identified from detailed
sensitivity analysis, e.g., Sobol analysis.
Key RAMPs that affect the homogenizedmaterial response are identified.
• Module IV generates functional forms of
PUCM constitutive coefficients as functions of RAMPs using machine learning
tools, operating on an extensive databaseof RAMPs and corresponding homogenized state variables resulting frommicromechanical simulations of the
SERVEs. The PUCMs are incorporated
in commercial FE software like ABAQUS
and ANSYS through user-defined material modeling interfaces, for microstructuresensitive structural response predictions.
Finally, uncertainty quantification is built
into the PUCM framework following a
Bayesian inference formulation [16,19,20]
to derive probabilistic microstructuredependent constitutive laws of themacroscopic response.

Titanium alloys, such as Ti-7Al, Ti-64 andTi-6242, are widely used in the aerospaceindustry, e.g. in turbine engine disks andaircraft panels. The fatigue life of thesematerials is strongly affected by various
descriptors in their heterogeneous polycrystalline microstructure. These descriptors
include crystallographic orientation andmisorientation, as well as morphologicalfeatures like grain size distributions as seenin the electron backscatter diffraction(EBSD) images of orthogonal surfaces of an
extruded sample of alpha-phase hcp Ti-7Al
alloy in figure 1(c,d). The PUCM-based
structural simulations along with post-processing by the parametrically upscaled crack
nucleation model (PUCNM) developed in[17,18], are used to study the effect of
microstructure on mechanical response andcrack nucleation probability of a prototype
turbine engine blade shown in figure 1(a,b).

A significantly reduced number of solution
variables, compared to direct numericalmicromechanical simulations, make PUCMbased simulations several orders of magnitude more efficient with good accuracy. The
PUCMs have already found great interest
in industry and government laboratories. The
length-scale bridging can yield significant
advantages in location-specific material
design, while the speed achieved with

2.1 RAMPs: Constitutive parametersof PUCM equations have an explicit
dependence on microstructural descriptors,
which are expressed as functions of RAMPs.
Sobol sensitivity analysis of the macroscopic
response with respect to microstructuraldescriptors of near alpha-Ti alloys in [14,15,
17] has demonstrated that the crystallographic texture, lattice misorientation, andgrain size distribution have considerable

parametric upscaling is advantageous formany data-driven design algorithms.
Two applications of the PUCM framework
are discussed in this article. The first is on
the modeling of deformation and fatigue
crack nucleation in structural components ofpolycrystalline titanium alloys. The second
is on the use of parametrically-upscaled
continuum damage mechanics (PUCDM)
models for composite materials withnonuniform microstructural distributions.

2 Predicting Fatigue Crack Nucleation in
Structural Components of Ti Alloys

15 iacm expressions 49/21

(a)

(b)

(c)

Figure 1:
Microstructure-sensitive structural-scale simulation results using the
UQ-PUCM integrated in ABAQUS software:(a) a jet engine compressor disk-blade assembly with 42 blades;(b) FE model of an 8o angular segment containing
one blade with roller constraints for axial symmetry;(c) EBSD images of orthogonal surfaces of an extruded
Ti-7Al alloy showing the extrusion direction (ED)and transverse directions (TD, TD’),(e) inverse pole figure maps delineating microstructural
crystallography and morphology as well as micro-textured
regions (MTRs) elongated along ED [20]influence on the homogenized elasto-plastic
response. The corresponding RAMPs,
used to characterize the microstructural
distributions for these alloys are itemized
below.
•

Texture Intensity Parameter andMaterial-Symmetry Axes (𝑔𝛼, 𝒗𝛼):
These correspond to the eigenvalues andeigenvectors of the symmetric, secondorder texture tensor 𝐈𝑡𝑒𝑥 that characterizes
the aggregated crystallographic 𝑐-axis
orientation of hcp crystals in a polycrystalline
microstructure. 𝐈𝑡𝑒𝑥 is calculated as aweighted average of the crystallographic(0001) poles [14,17].
• Lattice Orientation with respect toMaterial-Symmetry Axes (OMA𝛼𝛽):This RAMP accounts for the effect of the
distribution of crystallographic slip systemorientations measured with respect to thematerial symmetry axes given by the
eigenvectors 𝐯𝛼 of the texture tensor.
This RAMP represents the crystallographic
orientation distribution in a reduced form, bymeasuring the density of the easy <a>-typeslip systems with respect to the material
symmetry axes, and governs the functional
iacm expressions 49/21 16(d)dependencies of anisotropic plasticity.
• Grain-pair misorientation parameter(𝐴𝜃𝑚𝑖𝑠): This RAMP represents thefraction of grain pairs with less than athreshold value of the misorientation angle
𝜃𝑚𝑖𝑠 between their c-axes [114,17]. It
reflects the ease of slip transfer acrossdifferent grain boundaries in a polycrystalline
microstructure, and governs the strainhardening response.
• Mean and standard deviation of thegrainsize distribution (𝐷𝜇𝑔 , 𝐷𝜎𝑔 ):The mean (𝐷𝜇𝑔 ) and standard deviation(𝐷𝜎𝑔 ) of a lognormal grain-size distribution
constitute two RAMPs that influence theinitial yield and subsequent strain hardening
response in PUCMs.
The finite deformation PUCMs are selected
to represent various mechanical response
characteristics of Ti alloys such as
anisotropy, tension-compression asymmetry,and strain-rate dependency etc. that areconsistent with the micromechanicalcrystal plasticity simulations. Functional
expressions of the constitutive parameters
in terms of RAMPs using machine learning
have been determined in [14-20].
2.2 Uncertainty Quantification and
Propagation: The constitutive relations
are further augmented with uncertainty
quantification and propagation to create anovel UQ-PUCM framework [16,19,20].
The explicit representation of microstructuralvariables in the PUCM constitutive equations
makes them particularly suitable forincorporating uncertainties attributed to
the following sources.
1. Model reduction error (MRE): This is an
epistemic uncertainty stemming from thediscrepancy between the machinelearning-generated, reduced-orderfunctional forms of PUCM constitutive
parameters and the microstructuredependencies implicit in the
micromechanical model.
2. Data sparsity (DS): This is an epistemic
uncertainty in the microstructuredependent constitutive parameters dueto the finite size of the micromechanicsbased calibration dataset.
3. Microstructural variability (MSV): Thisaleatoric uncertainty is due to the naturalvariability of the microstructure.
Uncertainties due to MRE and DS areaccounted for by employing a Bayesianframework to calibrate the functional forms of
constitutive parameters from crystal plasticity
FEM-generated analysis data. MSV in thisformulation is represented by a stochastic
extension of the representative aggregatedmicrostructural parameters or RAMPs. For
computationally efficient propagation of
uncertainty from the three sources to timedependent material state and model output
variables, a Taylor series expansion-based
uncertainty propagation (UP) method isdeveloped. Figure 2(a) shows the implementation of UQ/UP in the UQ-PUCM framework. This framework offers a comprehensive and computationally tractable multiscale modeling framework, which addresses
uncertainties relevant to both the calibrationand prediction stages.
The UQ-PUCM is validated withexperimental data on constant strain-ratetensile tests, performed on extruded Ti-7Al[17]. Consistent with the experiments,

macro-scopic FE models of the test
specimens are generated with loadingaxes along extrusion (ED) and transverse
directions (TD) of the material, as shownin Figure 2(b). The RAMPs are calculated
from EBSD characterization data in Figure 1,
and are assigned to the FE mesh following
a spatial sampling method. FE simulations
are performed with UQ-PUCM integrated
within the ABAQUS code. The predicted
mean and 1s intervals of the Cauchy stressat the gauge sections of both the ED and TD
specimens are plotted and compared withexperimental data in Figure 2(c). The results
show a larger uncertainty in the predicted
response near the yield region with 1s
associated with the functional forms.
2.3 Multiscale Response and Fatigue
Crack Nucleation in Turbine Engine
Blades: The PUCM-based FE model isused to model an angular segment of theturbine engine disk, shown in figure 1(a,b).
All the nodes in the model are subjected tocentrifugal loading corresponding to anangular velocity (ω) of 7200 rpm for a totalof 200 hours. This loading corresponds toapproximately 6000 take-off operations
each lasting 2 minutes and models theoperating conditions of the disk.
Figure 2:(a) Flowchart showing the flow of information and propagation of
uncertainty in the development and prediction stages of UQ-PUCM,(b) macroscopic FE model of a tensile test specimen (TD) used in
UQ-PHCM based ABAQUS simulations, showing the true
(Cauchy) stress distribution at 8% strain, &(c) comparison of stochastic stress-strain response fromUQ-PHCM simulations (solid lines and 1s bounds), withexperimental results (markers) along ED (black) and TD (blue)

17 iacm expressions 49/21

(a)

(b)

(d)
Figure 3:(a) Distributions of RAMPs calculated by sampling of EBSD scans for microstructure characterization data,(b) contour plot of +3s upper bound of stochastic von-Mises stress (MPa) after 200 hoursof operation at ω = 7500 RPM,(c) mean and ± s bounds of the Von Mises stress and equivalent
The stochastic material characteristics and
RAMPs of the Ti-7Al alloy are shown in
Figure 3(a). The ABAQUS FE simulations
take approximately 85 minutes with 8
CPU cores. Figure 3(b) shows the contour
plot of the stochastic von-Mises stress in theneighborhood of the blade support after 200
hours of operation. The variation of stochastic mechanical fields is studied along a path
A–B in the contour plot along the base of theblade. The stochastic von-Mises stress andequivalent plastic strain along this path areplotted in Figure 3(c) with black and red lines
respectively. The shaded areas correspondto the 1s bounds of the fields. Analogous
to the development of PUCMs, parametrical-(c)ly-upscaled crack nucleation models or
PUCNMs have been generated in [17,18]
from a database of the grain-scale
probabilistic crack nucleation simulations
developed in [25,26]. In [17,18], theprobability of crack nucleation in PUCNMs
is obtained at the macro-scale as a functionof macroscopic mechanical variables suchas stresses, plastic strain, and also theunderlying RAMPs of the microstructure.
The PUCNM corresponds to a macroscopic
signature of fatigue crack nucleation in
grains of the underlying microstructure.
Figure 3(d) plots the evolution of thecrack nucleation probability (Pnucl) at two
points along the path A-B with different

Figure 4:(a) Loading and specimen configurations for SENB experiments on double-ply composite beam,(b) PUCDM-based damage evolution and X-ray images for different applied displacements,(c) micromechanical damage evolution ahead of the notch-tip, and(d) comparison of the evolution of inter-laminar delamination
as a function of the displacement

iacm expressions 49/21 18

microstructures. As a result of higher plasticstrain accumulation and the underlying
RAMPs, one of the microstructures undergoes earlier crack nucleation (Pnucl=0.95) incomparison with the other. This study illustrates the potential of the PUCM and
PUCNM-based simulations in giving insights
into the microstructural influence on material
response.

3 Parametrically-Upscaled
Continuum- Damage Mechanics(PUCDM) Model for Multiscale
Damage Evolution in Composites
Parametrically-upscaled continuum damage
mechanics (PUCDM) models have beendeveloped in [21-23] for multiscale modelingof damage and failure in composites withnonuniform fiber distribution. The PUCDM
models incorporate data-driven techniques
in a thermodynamical framework to explicitly
represent parametric forms of key
microstructural features and descriptors,
in addition to evolving damage variables.
A thermodynamically consistent damage
potential is introduced in the space of theforce conjugate of damage. Specifically, afourth-order damage surface tensor Pijkl isparametrically represented as functions of
the evolving material damage states, as well
as of key morphological descriptors of thecomposite microstructure in the form of
RAMPs. The functional forms are developed using ANN algorithms, operating on a
database of microstructure response variables from micromechanical simulations.
The PUCDM models have demonstrated
significant computational efficiency andaccuracy in comparison with othermultiscale approaches [21-23].
3.1 Identifying Representative
Aggregated Microstructural Parameters(RAMPs): The 2-point correlation function
S2fiber is chosen to uniquely characterize thecomposite microstructures. However, thisfunction cannot be directly used as RAMPs
in the PUCDM model on account of thelarge number of data points representing
this correlation. For efficient and uniqueparametrization of S2fiber for the RAMPs, theprincipal component analysis (PCA), is used
to derive a low dimensional representation ofhigh dimensional data. For a given data-set,
PCA extracts a reduced number of featureswith a high variance. The original data-set
can then be represented efficiently throughthe extracted principal components,
computed as eigenvectors of the covariancematrix of the S2fiber data-set.

3.2 PUCDM-based Modeling of SingleEdge Notched Bending (SENB) Tests for
Glass-Epoxy Composite Beams: The
PUCDM-based FE simulations of SENB
experiments for [0,90] double-ply S-2 glass
fiber/SC-15 epoxy matrix composite beams,
shown in figure 4(a), are conducted.
Damage predicted by the PUCDM model arecompared with experimental measurements.
Figures 4(b) depict the contours of the
PUCDM model damage tensor component
D11 for different applied displacements, andthe corresponding high-speed synchrotron Xray images. The transverse crack in the bottom ply evolves towards the ply interface,and subsequently leads to delamination
between the 0o and 90o plies. After theformation of a full transverse crack in the bottom ply, additional deformation primarily
induces further inter-ply delamination andexpansion of the damage zone in the top
region, causing a rapid loss of the loadbearing capacity. Microscopic damage
evolution at a point on the top surface ofthe beam is shown in figure 4(c). Matrix
damage is the primary mode of macroscopic
material degradation in this zone. The
predicted crack lengths (inter-laminardelamination) are compared with theexperimental measurements in figure 4(d),which falls within the range of experimental
measurements.
In summary, this article demonstrates thesignificant potential of the parametricallyupscaled constitutive models for multiscale
analysis of deformation and damage inmetallic and composite materials. Explicit
dependence on the microstructure, thecapability of retrieving damage response
across material length scales, significant
computational efficiency, and ease of
integrating with commercial software
makes the PUCMs a very important tool for
structural analysis and material design. l
Acknowledgments:
The author acknowledges thecontributions of his students
Dr. D. Ozturk, Dr. S. Kotha and Dr. X. Zhang
to the development of this framework.

“ ...demonstrates the significant potential of
the parametrically-upscaled constitutive
models for multiscale analysis of deformationand damage in metallic
and composite materials.”
19 iacm expressions 49/21

References[1] Doghri, I., Ghezal, M.E., Adam, L., Finite strain mean-field homogenization of composite materials withhyperelastic-plastic constituents, Int. J. Plast., 81, 40 – 62, 2016.
[2] A. Benssousan, J. L. Lions, and G. Papanicoulau. Asymptotic analysis for periodic structures.
North Holland, Amsterdam, 1978.
[3] Fish, J., Shek, K., Pandheeradi, M., Shephard, M.S., Computational plasticity for composite structures basedon mathematical homogenization: Theory and practice. Comp. Meth. App. Mech. Eng., 148, 53 – 73, 1997.
[4] Ghosh, S., Lee, K., Raghavan, P., A multi-level computational model for multi-scale damage analysis incomposite and porous materials, Int. J. Sol. Struc., 38, 2335 – 2385, 2001.
[5] Kouznetsova, V., Geers, M.G.D., Brekelmans, W.A.M., Multi-scale constitutive modelling of heterogeneousmaterials with a gradient-enhanced computational homogenization scheme,
Int. J. Num. Meth. Engg, 54, 1235–1260, 2002.
[6] Vernerey, F., Liu, W.K., Moran, B., Multi-scale micromorphic theory for hierarchical materials,
J. Mech. Phys. Solids, 55, 2603 – 2651, 2007.
[7] Feyel, F., Chaboche, J., FE2 multiscale approach for modelling the elasto-viscoplastic behaviour of long
fibre SiC/Ti composite materials, Comp. Meth. App. Mech. Eng., 183, 309 – 330, 2000.
[8] Zhang, X., Oskay, C., Sparse and scalable eigenstrain-based reduced order homogenization models forpolycrystal plasticity, Comp. Meth. App. Mech. Engg. 326, 241 – 269, 2017.
[9] Fritzen, F., Kunc, O., Two-stage data-driven homogenization for nonlinear solids using a reduced ordermodel. Eur. J. Mech - A/Solids, 69, 201 – 220, 2018.
[10] Gupta, A., Cecen, A., Goyal, S., Singh, A., Kalidindi, S., Structure–property linkages using a data science
approach: Application to a nonmetallic inclusion/steel composite system, Acta Mater., 91, 239 – 254, 2015.
[11] Paulson, N., Priddy, M., McDowell, D., Kalidindi, S., Reduced-order structure-property linkages forpolycrystalline microstructures based on 2- point statistics, Acta Mater., 129, 428 – 438, 2017.
[12] Le, B.A., Yvonnet., J., He, Q.C., 2015. Computational homogenization of nonlinear elastic materials usingneural networks, Int. J. Num. Meth. Engg., 104, 1061–1084.
[13] Liu, Z., Wu, C., Koishi, M., 2019. A deep material network for multiscale topology learning and accelerated
nonlinear modeling of heterogeneous materials, Comp. Meth. App. Mech. Engg., 345, 1138 – 1168.
[14] Kotha, S., Ozturk, D., Ghosh, S. Parametrically homogenized constitutive models (PHCMs) frommicromechanical crystal plasticity FE simulations, Part I: Sensitivity analysis and parameter
identification for Titanium alloys, Int. J. Plast., 120, 296-319, 2019.
[15] Kotha, S., Ozturk, D., Ghosh, S. Parametrically homogenized constitutive models (PHCMs) frommicromechanical crystal plasticity FE simulations: Part II: Thermo-elasto- plastic model withexperimental validation for titanium alloys, Int. J. Plast., 120, 320-339, 2019.
[16] Kotha, S., Ozturk, D., Smarslok, B., Ghosh, S. Uncertainty Quantified Parametrically HomogenizedConstitutive Models for Microstructure-Integrated Structural Simulations,Integ. Mater. Manuf. Innov., 9(4), 322-338, 2020.
[17] Ozturk, D., Kotha, S., Pilchak, A., Ghosh, S. Two-way multi-scaling for predicting fatigue crack nucleationin titanium alloys using parametrically homogenized constitutive models,J. Mech. Phys. Solids, 128, 181-207, 2019.
[18] Ozturk, D., Kotha, S., Pilchak, A., Ghosh, S. Parametrically homogenized constitutive models (PHCMs)
for multi-scale predictions of fatigue crack nucleation in Titanium alloys,
JOM: J. Miner. Met. Mater. Soc., 71(8), 2564-2566, 2019.
[19] Kotha, S., Ozturk, D., Ghosh, S. Uncertainty-quantified parametrically homogenized constitutive models(UQ-PHCMs) for dual-phase α/β titanium alloys, npj Comp. Mater., 6(1), 1-20, 2020.
[20] Ozturk, D., Kotha, S., Pilchak, A., Ghosh, S. An uncertainty quantification framework for multiscaleparametrically homogenized constitutive models (PHCMs) of polycrystalline Ti Alloys,
J. Mech. Phys. Solids, 148, 104294, 2021.
[21] Zhang, X., Gao, J., O'Brien, D. J., Chen, W. and Ghosh, S., Parametrically homogenized continuum
damage mechanics model for multiscale damage evolution in single-edge notched bending experimentsof glass-epoxy composites, Compos. Part B, 09409 , 2021.
[22] Zhang, X. and Ghosh, S., Parametrically homogenized continuum damage mechanics (PHCDM) Models
for unidirectional composites with nonuniform microstructural distributions,J. Comp. Physics, 435,110268, 2021.
[23] Zhang, X., O'Brien, D. J. and Ghosh, S., Parametrically homogenized continuum damage mechanics(PHCDM) models for composites from micromechanical analysis,Comp. Meth. App. Mech. Engin., 346(1), 456-485, 2019.
[24] Hill, R., On constitutive macro-variables for heterogeneous solids at finite strain.
Proc. Royal Soc., London, 326:131–147, 1972.
[25] Ozturk, D., Pilchak, A., Ghosh, S. Experimentally validated dwell, and cyclic fatigue crack nucleation
model for α–titanium alloys, Scripta Mater., vol. 127, pp. 15-18, 2017.
[26] Anahid, M., Samal, M., Ghosh, S. Dwell fatigue crack nucleation model based on using crystal plasticity
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iacm expressions 49/21 20

Practical Optimization
Philip E. Gill, Walter Murray &Margaret H. Wright
SIAM, Philadelphia, USA, 2019

B
O
O
K

ISBN: 978-1611975598, 401 pages, soft cover, $95 (List Price).
Contents: Preface; 1: Introduction; 2: Fundamentals; 3: Optimality conditions;
4: Unconstrained methods; 5: Linear constraints; 6: Nonlinear constraints;
7: Modelling; 8: Practicalities; Questions and answers; Bibliography; Index.
This 2019 edition appeared in the SIAM series Classics in Applied Mathematics. It isan exact reproduction of the original “Practical Optimization” book that was publishedin 1981 by Academic Press. Naturally a lot of research on optimization methods has
been done since the book first appeared, but all the fundamentals covered here arestill very relevant. The authors remark in the new Preface that even the basics ofdata science can be found in chapters 7 and 8.
Roughly, the book is divided into four parts. Chapters 1 and 2 include mathematicalpreliminaries. Chapter 3 provides a classification of optimization problems and(necessary and sufficient) optimality conditions for each type. Chapters 4 to 6 are
the heart of the book and present various optimization methods. Chapters 7 and 8
discuss computational and implementational issues.

R
E
V
I
E
W

The book is very rich in information, sometimes in an encyclopedic scale (updated to1981, of course). It includes algorithms and pseudo-codes, which allow the reader
to better understand the methods presented, and to write her own codes. Somesections that are of a specific character are marked with * and can be skipped if
desired. Many sections end with “Notes”, sometimes quite elaborate, as well as
comments on the relevant bibliography. Each chapter starts with a quote of a wellknown scientist or a public figure. My two favorite ones are both on p. 285: “Whenone comes down to particular instances, everything becomes more complicated”(Albert Camus, 1963), and “You can get it wrong and still you think that it’s all right”(John Lennon and Paul MaCartney, 1965).
The book has a simple appearance, and it seems that no big effort was made to
make it particularly attractive visually. There are a few black and white figures, mostly
to illustrate the basic principles. Since it is a tradition of this Book Review section todisplay more exciting figures, we show here some illustrations from PhD theses on
optimization methods, from recent years.
Chapter 1 is short and presents the general optimization problem. Chapter 2
discusses fundamentals, like errors, condition number, LU factorization, finite
differences, iterative processes, and more. Chapter 3 provides a detailed classificationof optimization problems and their optimality conditions. The optimization problems
include the Unconstrained Problem (UCP), Linear Equality Constraints Problem (LEP),
Nonlinear Equality Constraints Problem (NEP), Linear Inequality Constraints Problem

by

Dan Givoli
Technion Israel Institute ofTechnology
givolid@technion.ac.il

Philip E. Gill

Walter Murray

Margaret H. Wright
21 iacm expressions 49/21

Figure 1:
Figure taken from the 2012 PhD thesis of
Mathieu Balesdent,“Multidisciplinary Design Optimization of Launch
Vehicles”, at the École Centrale de Nantes,
showing the tactic of how to graduallyzoom in on the minimizer(LIP), Nonlinear Inequality Constraints Problem (NIP),
Linear Programing (LP), Quadratic Programing (QP),
and more.
Chapter 4 is dedicated to methods for unconstrained
minimization. The order of the sections seems a bit
peculiar – discussion on non-smooth functions appears
before that on smooth functions, and the calculation of
the step size comes before the determination of thesearch direction. Here and in other places in the book,the authors choose to go from the more generalsituation to the more basic one, and not in the opposite
direction as in many other books. One may argue
about the merit in this approach, but it seems that thiswas done here thoughtfully by the authors, as thewhole book is written in a careful and didactic way.
This chapter includes a discussion on sparse discrete
Newton and on Conjugate Gradients, among othermethods.
Chapter 5 concerns optimization under linear
constraints and discusses LP and QP algorithms
among other things. Chapter 6 discusses nonlinear
constraints, including penalty and barrier function
methods, augmented Lagrangian methods and others.
In Chapter 7, the authors give important practicaladvice on issues related to the optimization process.
The same problem can be presented in more than one way, and one way may be
more amenable to computation or more efficient than some others. The advice given
here includes (a) ways to avoid unnecessary discontinuities, (b) ways to simplify or
remove some of the constraints, and (c) the importance of good scaling. Chapter 8
deals with some numerical difficulties such as overflow, insufficient decrease in the
merit function, lack of progress in the iterative process, and more. A nice sentencethat appears on p. 324 is: “Ideally, the user should not need to perform a detailed
investigation to determine why a routine has failed, since a good routine should
provide some reason for an unsuccessful termination.”
In summary, this book is rich with information, and is excellent as a reference bookon basic optimization methods. A bit surprisingly, it is not significantly outdated as
one might expect from the fact that it was
written 40 years ago. Yet, a teacher or astudent looking today for a good textbook
on optimization methods should probably
look elsewhere. l
Figure 3:
Figure taken from the 2018 PhD thesisof Jan H.K. Haertel, “Design of Thermal
Systems Using Topology Optimization”,
at the Denmark Technical University,
illustrating three types of optimization
iacm expressions 49/21 22

Figure 2:
Figure taken from the2016 PhD thesis ofMaria Pires Pacheco,“Fast Reconstructionof Compact ContextSpecific Metabolic
Network Models”,
at the university of
Luxembourg, showing
an example oflinear programingfor all inclusions under
IACMM
please contact
Mahmood Jabareen
cvjmah@technion.ac.il

uring the current year, the Israel Association for Computational Methods in
Mechanics (IACMM) held one symposium due to the restrictions imposed by the
Covid-19 pandemic. In particular, the 48th Israel Symposium on Computational
Mechanics (ISCM-48) was held on November 11th 2021 at Technion – Israel
Institute of Technology, Haifa, and the local organizers were Prof. Oded Amir andProf. Mahmood Jabareen.

D

The interesting opening lecture was given by the invited speaker Prof. Shaul
Markovitch from the Computer Science Department at Technion – Israel Institute ofTechnology, and titled "Are general-purpose autonomous robots coming soon?"
Figure 1 shows Prof. Markovitch with the IACMM executive council and the localorganizers of ISCM-48. In his talk, Prof.
Markovitch presented the current state of general-purpose robots and aspects of artificial
intelligence. Also, Prof. Markovitch proposed
the missing components that are needed forbuilding such general purpose-robots.

Figure 1:
Group photo of IACMM executive council,
invited speaker and organizers of ISCM-48.
Left to Right: D. Givoli, J. Tal, P. Bar-Yoseph,O. Amir, S. Markovitch, M. Jabareen, E. Priel

The 48th Israel symposium included 8 other lectures, presented by researchers
from academia. These lectures spanned over a broad spectrum of computational
mechanics topics such as machine learning, material modeling at different scales,
structural topology optimization, buckling of plates, and more. The ISCM-48 wasconcluded by a tutorial lecture given by Dr. Yuri Feldman from the Department of
Mechanical Engineering, Ben Gurion University – the title of his tutorial was"Recent trends and progress in the immersed boundary method." In his tutorial,
Dr. Feldman presented the fundamentals of several basic formulations of theimmersed boundary method. Also, Dr. Feldman, elucidated the latest progresses
and challenges related to numerical simulation of various complex physicalphenomena using the immersed boundary method.
During the ISCM-48th, a competition for the best presentation was held and thewinner Mr. Adar Kahana, a PhD student
from the Department of Mathematics at
Tel Aviv University. The title of Mr. Kahana's
talk was "Deep-learning method for locating
sources in underwater acoustics with highnoise." Figure 2 shows Mr. Kahana during
his talk. lFigure 2:
Mr. Kahana, the winner of the ISCM-48
competition for the best presentation,
during his talk
23 iacm expressions 490/21

Re-elected UKACM president
uring the Executive Committee meeting of UKACM, held on 14th April 2021,
it was announced that Prof Rubén Sevilla has been unanimously elected tocontinue as President of UKACM until December 2024.

D

UKACM 2021 Conference
he 2021 conference took place on 14-16 April 2021. Organised by Dr Marco
Discacciati (Loughborough University), the conference followed an online format,
with 78 participants delivering live talks.

T

UKACM School
As it is tradition in UKACM, a School was organised before the start of theconference. This year two topics were covered in the UKACM School.
•
Model Order Reduction methods.
(Prof. Elías Cueto and Dr Alberto Badías, University of Zaragoza, Spain)•'Weak form' in many flavours. Sustainable development of research code forcomplex engineering problems(Dr Lukasz Kaczmarczyk and collaborators, University of Glasgow, UK).
UKACM Plenary Speakers
Three plenary lectures at the conference were delivered by the following
distinguished researchers:

Prof Paola F. Antonietti(Politecnico di Milano,
Italy)
Numerical models forearthquake ground motion
and seismic risk assessment

Prof Elías Cueto(University of Zaragoza,
Spain)On the role of machinelearning in computational
mechanics

Prof Joaquim Peiró(Imperial College London,
UK)
A variational approach tohigh-order curvilinear
mesh generation
and adaptation forcompressible flows

UKACM Conference Awards
The following delegates received the three UKACM conference awards:•
Fabiola Cavaliere from Universitat Politècnica de Catalunya and Swansea
University received the Mike Crisfield Prize for the paper “A nonintrusive
reduced order method for the NVH assessment and automotive structuraldynamics”.
•
Eki Febrianto from Cambridge University received the prize to the best
presentation by a post-doctoral researcher for the paper “The statistical finite
element method for physics-informed digital twins”.
•
Massimo Sferza from the University of Nottingham received the prize to thebest presentation by a PhD student for the paper “Global-local multidisciplinary
optimisation of aircrafts”.

iacm expressions 49/21 24

for all inclusions under UKACM please contact
Rubén Sevillar.sevilla@swansea.ac.uk

UKACM Conference Proceedings
The proceedings of the 2021 UKACM conference can be accessed athttps://repository.lboro.ac.uk/projects/UK_Association_for_Computational_Mechanics_
UKACM_Conference_2021/113478

2021 Roger Owen PhD Thesis Award
The winner of the 2021 Roger Owen PhD Thesis Award was Dr Robert Bird. l

UKACM 2022 Conference

he 2022 UKACM conference will be organised by Dr Jelena Ninic, Dr Matteo
Icardi and Dr Kristoffer Van Der Zee, from the University of Nottingham. The
Conference and School will take place between 20th-22nd of April 2022.

T

The following invited speakers will deliver a plenary lecture in the UKACM 2022
Conference:•Prof. Dr.ir. Dr.h.c. René de Borst, University of Sheffield•
Prof. Dr.-Ing. Stefanie Elgeti, Vienna University of Technology•
Prof. Dr.-Ing. Dominik Schillinger, Leibniz Universität Hannover•
Dr Emilio Martinez-Paneda, Imperial College London
The school will focus on:•
Computational phase-field modelling - Dr Kris van der Zee•
Immersed finite element methods - Prof. Dr.-Ing. Dominik Schillinger•
Optimization - Dr Ender Özcan
This conference will also mark the start of the 30th Anniversary of UKACM. l
25 iacm expressions 49/21

USACM Holds 16th National Congress
on Computational Mechanics
as Virtual Eventhe 16th U. S. National Congress on Computational Mechanics (USNCCM16)
was held as a fully virtual event July 26 - 29, 2021. The conference was widely
received by the computational mechanics community with over 1240 conference
registrants that represented 36 countries. USNCCM16 also had 459 student
participants (459) and a record number of women researchers (181) who
participated.

T

USNCCM16 offered a strong technical program with 73 minisymposia thatcomprised 269 technical sessions. Over 1075 technical abstracts were presented
during the four days of the conference. The Congress featured four Plenary andsix Semi-plenary lectures that covered the broad spectrum of Computational
Mechanics. Special efforts were made to preserve the in-person flavor ofinteraction with the technical sessions being broadcast live. Several networking
features were also available.
Other highlights of the conference included:m A series of mid-day talks celebrating the 30th year of holding USNCCMs.
The featured speakers were Professors Bob Taylor, J. Tinsley Oden, andTom Hughes who presented reflections and history of USACM.
m A minisymposium honoring Prof. J. Tinsley Oden's contributions toComputational Mechanics.
m The USACM-FRG and FRC-IACM held a Women’s Networking Event featuring
Dr. Jian Cao as a guest speaker and presented several awards for Best
Female Presentation (see frgusacm.org for the list of winners).
m Over 30 posters were presented virtually in a student poster competition.
The winners were announced during the Closing Ceremony and their names
can be found at 16.usnccm.org/poster_competition.
A panel discussion on Navigating the Emerging Landscape of Computational
Mechanics featuring experts from academia, national laboratories and federal
agencies was held. Another panel discussion on the Role of Computational
Mechanics in Industry, discussing the fostering of fruitful mechanics collaborations
between academia, national labs, and industry, and the opportunities for students
in industrial careers that leverage computational mechanics was presented.
To read more about this discussion, go to the USACM website, usacm.org. l

USACM Upcoming Events
further details at usacm.org
m
m
m
m
m

The Role of Mathematical and Computational Modeling in Cancer Research, January 10-12, 2022,(virtual conference), sites.google.com/usacm.org/bio-mcr/home
Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling (MLIP),
August 18-19, 2022, Arlington, VA, sites.google.com/usacm.org/uq-mlip/home
Meshfree and Novel Finite Elements with Applications, September 25-27, 2022,
Berkeley, California; mfem2020.usacm.org
Isogeometric Analysis, November 6-9, 2022,
Banff, Canada; iga2022.usacm.org
17th U. S. National Congress on Computational Mechanics, Albuquerque, New Mexico,
July 23-27, 2023, 17.usnccm.org liacm expressions 49/21 26

for all inclusions under USACM please contact
admin@usacm.org

USACM 2021 Awards
John von Neumann Medal – Jacob Fish,
Columbia University
For his sustained and seminal contributions to the field of multiscalecomputational science and engineering and for its major impact on industry

The Belytschko Medal – Gordon Johnson,
Southwest Research InstituteIn recognition of pioneering and lasting contributions to high-ratecomputational solid dynamics, particularly constitutive models formetals and brittle materials and explicit methods
for finite element and meshfree particle analysis

Thomas J.R. Hughes Medal – Qiang Du,
Columbia University
For his numerous innovative contributions to computational
physics and computational fluid mechanics, and his unwavering
service to the scientific computing community

J. Tinsley Oden Medal – Somnath Ghosh,Johns Hopkins University
For outstanding fundamental contributions to Computational
Mechanics of Materials through the development of innovative
methodologies in spatio-temporary multi-scale modeling of heterogeneousmaterials transcending the Mechanics and Materials communities

Gallagher Young Investigator Medal – John Evans,
University of Colorado-Boulder
For contributions to isogeometric analysis, structure-preserving discretizations,multiscale and stabilized methods, and immersed methods with application to
fluid mechanics, structural mechanics, and fluid-structure interaction

USACM Fellows
Harold Park, Boston University
For important contributions to atomistic/continuum coupling methods,
computational modeling of surface effects on nanomaterials, long timescale
methods for atomistics, the mechanics of two-dimensional
materials, and computational soft matter mechanics.

James Stewart, Sandia National Laboratories
For leadership and service to the USACM community,
especially in the area of uncertainty quantificationand data-driven modeling. l

27 iacm expressions 49/21

New GACM Boardt the annual General Assembly held at the end of last year, the new GACM
board was elected for the next four-year term. The new board is composedof the following members:A

M. Behr (RWTH Aachen, president), G. Meschke (RU Bochum, vice president),
M. Kaliske (TU Dresden, previous president), S. Klinkel (RWTH Aachen, treasurer),T. Münz (Dynamore GmbH),
S. Leyendecker (FAU ErlangenNuremberg), C. Birk (U Duisburg-Essen),
L. Scheunemann (U Duisburg-Essen,
junior researcher), N. Hosters(RWTH Aachen, general secretary)
At this point, we would like to take theopportunity to thank the past president,
Michael Kaliske, for his dedicatedservice over the past four years.

Figure 1:
First Board Meeting

9th GACM Colloquium on
Computational Mechanics 2022
Essen, Germany

Colloquium Chairpersons
D. Brands, H. Gravenkamp, L. Scheunemann, C. Schwarz, A. Schwarz
The 9th GACM Colloquium on Computational Mechanics for Young Scientists
from Academia and Industry will take place from September 21 to 23, 2022 in
Essen, Germany. The colloquium is jointly hosted by the Institute of Structural
Analysis of Plates and Shells and the Institute of Mechanics at the University ofDuisburg-Essen.
The GACM Colloquium is a biennial conference that brings together young
researchers in the field of computational mechanics. This event provides excellent
opportunities for students and postdocs to present their work, to acquire newknowledge in the field of numerical modeling in engineering and science, and toestablish a network of colleagues and peers. The colloquium consists of minisymposia — organized by selected young scientists — as well as a poster session
and several plenary lectures.
We are pleased to announce the plenary lectures given by Prof. Swantje Bargmann(BU Wuppertal), Prof. Laura De Lorenzis (ETH Zürich/TU Braunschweig),
Prof. Marc-André Keip (U Stuttgart) and Prof. Otto von Estorff (TU Hamburg).
We would like to invite you to attend the 9th GACM Colloquium.
Detailed information will be announced here: https://colloquia.gacm.de/.

iacm expressions 49/21 28

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

GACM Best PhD Award
In 2012, GACM has successfully established an award for young academics,
namely the GACM Best PhD Award. It is our great pleasure to announce thatthe outstanding doctoral thesis of Dr.-Ing. Maximilian Grill is honored with thisaward for the year 2020.
The thesis "Computational Models and Methods for Molecular Interactions ofDeformable Fibers in Complex Biophysical Systems" of Maximilian Grill was
conducted at Technical University of Munich and has been performed under theacademic supervision of Prof. Wolfgang Wall.
The award will be officially conferred at the next GACM Colloquium in Essen in 2022.

Figure 2:
Dr.-Ing. Maximilian Grill
Best PhD Award

Dr. Grill’s PhD thesis was concerned with novel models and methods for molecularinteractions of deformable fibers such as van der Waals adhesion,
electrostatic effects, and steric repulsion. Bringing together
fundamental concepts from the disciplines ofmolecular biophysics and computational
mechanics in a novel, unprecedented
way, Dr. Grill’s work develops conceptually
new methods that allow to investigate so
far intractable problems in real biological systems on the nano- and microscale.
Several examples of suchapplications are presented in his thesis.

Figure 3:
Self-interaction of a mucin
filament carrying a complex
line charge distribution

The key idea of the methodological part is to describe the molecular two-bodyinteraction in a dimensionally reduced manner, which is inspired from and thus fully
consistent with using beam theory as a model for the slender deformable fibers. This
paradigm shift from atom-atom to section-section interaction potentials (SSIP) leads
to a beam-beam interaction formulation, which isthe first of its kind and yields a speedup of morethan three orders of magnitude. In the importantcase of very short-ranged interactions, Dr. Grill
showed that this can even be taken one stepfurther by using section-beam interaction potentials(SBIP) to achieve an additional 4x speedup. Both,the first principles of molecular interactions as wellas geometrically exact 3D beam theories, involvea high mathematical complexity and are still highly
active fields of research. Dr. Grill selected andcombined the latest and most promising techniques
in his thesis. For the analytical derivation of thepivotal SSIP and SBIP laws (starting from the firstprinciples), Dr. Grill succeeded to find accurate,
simple expressions for the challenging four- or evenfive-dimensional integrals over both interacting
bodies (at arbitrary distance and mutual orientation). His choice to consider genericinteraction potential laws and different scenarios such as short- and long-ranged
interactions additionally increased the mathematical complexity, yet significantly
extends the versatility of the novel approaches. This is demonstrated by the variety
of applications. To mention one last aspect, Dr. Grill resolved thecritical numerical issues associated with the characteristic
singularity of molecular interaction laws at zero separation
by developing a highly effective, tailor-maderegularization scheme. l

Figure 4:
Hindered diffusion of acharged nanoparticle througha biopolymer fiber network

Figure 5:
Pulling off two adhesive, nanofiber-grafted surfaces
29 iacm expressions 49/21

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 2022.

S

SEMNI’s prize to young researcherscommemorates the legacy of the lateProfessor Juan C. Simó in the field of
numerical methods. The committee wasformed in this occasion by Ignacio Romero(president), Manfred Bischoff (University of
Stuttgart), Antonio Gil (University ofSwansea), Alessandro Reali (Universityof Pavía) and Laurent Stainier (Universityof Nantes).
In this occasion, the awardee is
Dr. Marcos Latorre (Yale University),
for his contributions to computational
nonlinear solid mechanics with emphasis
on applications to cardiovascularbiomechanics and mechanobiology.
The Committee has valued the breadthof his research activities and the variety and extent of his national and international
research collaborations. l

SEMNI best PhD award
2021
he SEMNI best PhD thesis award committee, composed by Francisco
Montáns (President, Polytechnic University of Madrid), Marino Arroyo(University of Catalan), Estefanía Peña (University of Zaragoza), Esther Reina(Servile University) and Rena C. Yu (University of Castilla-La Mancha), selected
the following doctoral thesis defended
during 2020 for the best PhD award:

T

Dr. Alberto Baldías Herbera, Universityof Zaragoza, “Simulated reality:
physics-based mixed and augmented
reality for intelligence augmentation”,
supervised by David González and
Icíar Alfaro . l

iacm expressions 49/21 30

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

Memoriam:
Manuel Casteleiro

t is with great sadness that SEMNI informs the death of Professor ManuelCasteleiro. He died on April 22, 2021, at his home in A Coruña at 74. A Civil
Engineer from the Polytechnic University of Madrid, Casteleiro got a Master andPh.D. from the University of Wisconsin-Milwaukee and Northwestern University in
the USA, respectively. Upon his return to Spain in 1975, he became a professor
at the Civil Engineering School, the Polytechnic University of Catalonia (UPC).
He contributed actively and significantly to the establishment and consolidationof the new school. During this period, he also served as vice-rector for teaching
staff, director, and founder of the Department of Applied Mathematics III. In 1992
he moved to the University of A Coruña, Galicia. His talent, experience, andvision left their mark on this new institution, of which he was also vice-rector,
and was crucial in the founding stage of this new school, of which he was
director between 2005 and 2013. Upon retirement in 2017, he continued towork as an emeritus professor until a few weeks before his death.

I

Manolo, fondly known by his family and friends, was an extraordinary instructor
who greatly influenced the formation of more than 40 promotions of engineers
in the two schools to which he dedicated his life. A brilliant researcher withinternational fame, he promoted numerical methods and their applications inengineering and trained scientists who today lead top-level research groups.
He fostered the promotions of CIMNE (the International Center for Numerical
Methods in Engineering) and SEMNI (the Spanish Society of Numerical Methods
in Engineering). In the 90s, he also led a fruitful alliance with APMTAC (thePortuguese Association of Theoretical, Applicational, and computational
Mechanics) for the joint organization of the national congresses of bothcountries.
For the members of SEMNI, Manolo not only chaired SEMNI between 2003
and 2008, but he is also a reference figure in our community. He has inspired
many members with hisleadership, example, andcharisma. He served oursociety with dedication andhad been a dear companion
for all its members. In 2017
he received the Olgierd C.
Zienkiewicz award, the highest
recognition of SEMNI for his
research career.
Great personalities are usuallybeacons that shine beyond thepurely professional. Manolo
was an inspiring man. Thoseof us who were lucky enoughto work with him learned a lot
from his side. We will misshim and remember him. May
he rest in peace. l

31 iacm expressions 49/21

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 withe-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

The JACM elected a new executive council in March 2021. Hiroshi Okada continues
to serve as the president of JACM for next three years. The former secretary
general, Professor Ryuji Shioya, continues his service as the vice president.
The new executive council members for 2021–2023 are as listed below:
President:
Professor Hiroshi Okada, Tokyo University of Science, hokada@rs.noda.tus.ac.jp,
http://www.rs.noda.sut.ac.jp/me/laboratories/okada_laboratory.html
Vice Presidents:
Professor Ryuji Shioya, Toyo University
shioya@toyo.jp, http://www2.toyo.ac.jp/~shioya/shioya.html
Professor Kenji Takizawa, Waseda University
Kenji.Takizawa@waseda.jp, https://www.jp.tafsm.org/
Secretary General: Professor Yuichi Tadano, Saga University
tadano@me.saga-u.ac.jp, http://sentan.me.saga-u.ac.jp/profile_tadano_eng.html

Figure 1:
Hiroshi Okada (President), Ryuji Shioya (Vice President), Kenji Takizawa (Vice President), Yuichi Tadano(Secretary General), April 2021–March 2024.
On July 29, 2021, the 2021 JACM annual meeting and award ceremony were held
online. In this year, JACM inducted ten new honorary members. Before the award
ceremony, new honorary members were introduced. They have distinguishedrecords of researches and services to computational mechanics related professionalsocieties. The new honorary members are:Figure 2:
Professor
Thomas J. R. Hughes(The University of Texas
at Austin)making his short speechProfessor Ichiro Hagiwara (Professor Emeritus, Tokyo Institute of Technology andDistinguished Professor, Meiji University), Professor Koichi Hashiguchi (Professor
Emeritus, Kyushu University and MSC Software), Professor Thomas J. R. Hughes(The University of Texas at Austin), Professor Hiroshi Kanayama (Professor
Emeritus, Kyushu University), Professor Masanori Kikuchi (Professor Emeritus,
Tokyo University of Science), Professor Yutaka Miyake (Professor Emeritus, Osaka
University), Professor Noriyuki Miyazaki (Professor Emeritus, Kyushu University and
Kyoto University), Professor Tayfun E. Tezduyar (Rice University), Professor Takashi
Yabe (Professor Emeritus, Tokyo Institute of Technology).
Each of new honorary members gave a short speech, after their introductions(Figure 2, Professor Thomas J. R. Hughes making his short speech).
Following the introduction of the new honorary members, the award ceremony took
place. There are three categories of JACM Awards. They are the JACM
Computational Mechanics Award which is the highest award, JACM Fellows Awardand the JACM Young Investigator Award.

iacm expressions 49/21 32

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

(b)

Professor Jiun-Shyan (J. S.) Chen (University of California, San Diego)
and Professor Shu Takagi (The University of Tokyo) (Figure 3).
Figure 3: Computational Mechanics Award:(a) Professor Jiun-Shyan (J. S.) Chen
(University of California, San Diego) and(b) Professor Shu Takagi (The University of Tokyo)
The recipients of the JACM Fellows Award were Professor Taiji Adachi(Kyoto University), Professor Atsuya Oishi (Tokushima University),
Professor Akiyuki Takahashi (Tokyo University of Science), Professor
Shinya Taketomi (Saga University) and Professor Amane Takei(Miyazaki University) (Figure 4).

(a)
(b)
(c)
(d)
(e)
Figure 4: Fellows Award:(a) Professor Taiji Adachi (Kyoto University), (b) Professor Atsuya Oishi (Tokushima University),(c) Professor Akiyuki Takahashi (Tokyo University of Science), (d) Professor Shinya Taketomi (Saga University) and(e) Professor Amane Takei (Miyazaki University)
The winners of JACM Young Investigator Award were Professor Shunhua Chen (Sun Yat sen University),
Dr. Daisuke Hotta (Meteorological Research Institute, Japan Meteorological Agency) and Professor Yuto Otoguro(Tokyo University of Science) (Figure 5).
(a)
(b)
(c)
Figure 5: JACM Young Investigators Award:(a) Professor Shunhua Chen
(Sun Yat-sen University),(b) Dr. Daisuke Hotta(Meteorological Research Institute, Japan
Meteorological Agency)and (c) Professor Yuto Otoguro(Tokyo University of Science)

At the end of the 2021 JACM meeting, wetook a group photo (Figure 6). Although I
believe that the 2021 JACM meeting was amemorable event for all the participants, I
hope to have our 2022 meeting in person. l

Figure 6:
Group Photo (All the participants)
33 iacm expressions 49/21

WCCM-APCOM2022
he 15th World Congress on Computational Mechanics & 8th Asian PacificCongress on Computational Mechanics (WCCM-APCOM 2022) will be held in
Yokohama, Japan, July 31 to August 5, 2022 (https://www.wccm2022.org).

T

The WCCM-APCOM Congress is co-organized by The International Association
for Computational Mechanics (IACM) and The Japan Society for Computational
Engineering and Science (JSCES) in cooperation with The Asian Pacific
Association for Computational Mechanics (APACM) and Japan Association for
Computational Mechanics (JACM).
The plans to hold an “on-site” congress with featured plenary and semi-plenary
speakers, over 400 mini-symposia with keynote lectures, exhibitions from various
sponsors, and other wonderful events, expecting the end of COVID-19 crisis untilthen.

Key days (for the time being)
2021 November 15:
2022 March 1 - May 10:
May 10:
May 10:
May 31:
June 1 - July 6:
iacm expressions 49/21 34

Deadline for Submission Abstracts
Early registration period
Deadline for speaker registration
Deadline of physical or virtual attendance selection
Deadline for submission of extended abstract
Video submission periodJunji Kato
junjikato@nagoya-u.jp“On-site” or “Online” conference?
The local organizing committee believes that face-to-face conference at the venue isthe best way to know/communicate each other and to expand future personal connections over the world, in particular for young researchers. Based on this philosophy, the WCCM-APCOM 2022 is organized as the “on-site” conference at the
moment. However, the format is subject to change according to situation of the
COVID-19 pandemic.

The JSCES Grand Prize
2020
Prof. Jiun-Shyan Chen (Figure 2) of University of California, San Diego, received
the JSCES Grand Prize 2020 for his outstanding contributions in the field ofcomputational engineering and sciences. The awarding ceremony and his special
lecture were held during the annual conference 2021 (online). The awarding
ceremony and the special lecture of Prof. Antonio Huerta (Figure 3) of Universitat
Politècnica de Catalunya, the awardee of JSCES Grand Prize 2019, were also held
together since the annual conference 2020 was canceled due to COVID-19.

35 iacm expressions 49/21

for all inclusions under GIMC please contact
Sonia Marfia
sonia.marfia@uniroma3.it
Gruppo Italiano di
Meccanica Computazionale

AIMETA 2021 Conference
Postponed to 2022
he Italian Association of Theoretical and Applied Mechanics (AIMETA) postponed
its conference, AIMETA 2021, to September 2022.
The conference will be held in Palermo (Sicily, Italy).
More information and the conference dates will soon be available at thefollowing linkT

https://www.aimeta.it/index.php/notizie/191-congresso-aimeta

GIMC Best PhD Thesis Award
The GIMC Best PhD Thesis Awards committee, composed by Massimo Cuomo(Università di Catania), Lorenza Petrini (Politecnico di Milano), Alberto Salvadori(Università di Brescia) and Giuseppe Vairo (Università di Roma Tor Vergata),
selected the following theses, defended in 2020, as the best Theses in
Computational Solid and Fluid Mechanics:-

Dr. Giulia Bertaglia, Università di Ferrara, “1D augmented fluid-structureinteraction system with viscoelasticity: from water pipelines to blood vessels”.
This thesis was also selected as candidate to the ECCOMAS PhD Award,-

Dr. Francesco Liguori, Università della Calabria, “Optimal design andnumerical modelling of imperfection sensitive shell structures”.

News from AIMETA groups
The AIMETA groups of Biomechanics (GBMA), Computational Mechanics (GIMC) and Mechanics ofMaterials (GMA) have organized the on-line event “Giovani alla ricerca” held on 7-8 June.
“Giovani alla Ricerca” has moved from the desire to give space and visibility to the younger researchersof the three groups, in a period of limited scientific relationship due to the Covid pandemic, offering theman opportunity for sharing ideas, comparisons and critical discussions. In particular, the meeting has
allowed to present to the scientific community, the best PhD Theses discussed in the two years 2020-2021
rewarding those young people who, despite the difficulties of these days, have continued to work effectively
in the fields of Biomechanics, Computational Mechanics and Mechanics of Materials.

iacm expressions 49/21 36

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 counts the following Central European countries (in alphabetical order):Austria, Bosnia and Herzegovina, Croatia, Czech Republic, Hungary, Slovakia andSlovenia. In 2021, CEACM has elected a new Executive Committee that consists of the
President, Adnan Ibrahimbegovic, the Vice-President, Janos Logo and the Past-President,
Bernhard Pichler.

T

The scientific activities of CEACM in 2021 concern short courses and a scientificconference ECCOMAS MSF 2021, jointly organized by University of Split, University ofSarajevo and UT Compiegne.
The short course, entitled “Current Research on Solids & Fluids: Computations, FE Code
Coupling, Model Reduction, Probability…” (URL: http://gf.unsa.ba/eccomas-msf-2021/
index.php/preconference-course/), was held just before the conference, June 27-29,
2021. The course participants were doctoral students from CEACM and other European
countries. The course lecturers: A. Ibrahimbegovic (UTC), H.G. Matthies (TUBs),
N. Limnios (UTC), A. Ouahsine (UTC), J.L. Perez-Aparicio (UPV) and F. DeVuyst (UTC)provided an extensive review of numerical models for computational solid and fluid
mechanics, modern developments in model reduction, probability aspects and uncertainty
quantification, coupling with thermal, electric and magnetic fields and their interactions.
In 2021, CEACM sponsored a scientific conference ECCOMAS MSF 2021 –MultiscaleComputational Methods for Solids and Fluids, which was held in Split, June 30- July 2,
2021. This was the 5th conference in series organized by Adnan Ibrahimbegovic, thistime in collaboration with Mijo Nikolic, professor at University of Split. The meeting
attracted close to hundred scientific presentations, with total of
208 contributing authors. The conference was held in hybrid
format with 30% of participants present onsite and the remaining
online. The conference provided a platform for exchange
among leading specialists in analysis and design of complex
engineering structures and systems, coming from aerospace,civil and mechanical engineering, material science, and in thedesign and analysis of numerical algorithms from appliedmathematics. The main goal was elaborating the multi-field
and multi-physics approach, which has significantly modified
previously firm frontiers among these traditional engineering
disciplines. A series of introductory distinguished lectures on thefirst day of this conference were scheduled with: H. Matthies and
D. Dinkler from Germany, P. Ladeveze, R. Ohayon and A. Ibrahimbegovic from France,
J.L. Perez-Aparicio from Spain, P. Pimenta and A. Couthino from Brazil, C. Hellmich
from Austria and P. Travalusci from Italy. Ten different Mini-Symposia were organized
covering various topics on: Resiliance, Fracture, Experimental Aspects, Environment,Internal Flows, Multi-phase flows, Steel Structures, Isogeometric and Discrete elements.
The ECCOMAS MSF 2021 Proceedings are available for download at(URL: http://gf.unsa.ba/eccomas-msf-2021/Eccomas_MSF_2021.pdf).

Figure 1:
ECCOMAS MSF
2021 Conference
onsite participants

Figure 2:
ECCOMAS MSF
2021 Conference
final meeting oforganizing committee

37 iacm expressions 49/21

Chair:Pr. Francisco Chinesta
Co-chairs:
Pr. Rémi Abgrall, Pr. Olivier Allix, Pr. David Néron, Pr. Michael Kaliske
Local organizing committee: Pr. Rémi Abgrall, Pr. Olivier Allix, Pr. Delphine Brancherie,
Pr. Francisco Chinesta, Dr. Luc Laurent, Dr. Cécile Oliver-Leblond, Pr. David Néron

The joint 14th World Congress in Computational Mechanics and ECCOMAS Congress
was planned to take place in Paris in July 2020 with more than 5000 presented papers. The
tragedy of the covid-19 pandemic has made impossible to organize the ECCOMAS-WCCM
congress in its standard face-to-face format. The organizing committee finally proposed thefirst ECCOMAS-WCCM congress organized in a fully digitalized format.
This very special event received an enthusiastic response from all the community, bringing
together more that 3000 participants of 30 countries, over 280 mini-symposia (MS)corresponding to almost 2500 presented papers. The presentations were pre-recorded, making
them accessible to a wider audience than in the traditional format. They were available online,
along with all the special events organized during the congress week, till end of August 2021.

iacm expressions 49/21 38

for all inclusions under CSMA please contact
David Néron
david.neron@ens-paris-saclay.fr

The week of the congress (January 11-15, 2021) was the occasion to organize a series of
special events going from the traditional plenary (4) and semi-plenary lectures (24) to more
special events as the ECCOMAS young investigators event, the IACM Female Chapter eventor the round table on future challenges in computational mechanics. All along the congress
week, daily live meetings around a plenary conference, 4 semi-plenary conferences anddiscussion sessions dedicated to mini-symposia were offered. They were the occasion ofvery rich and fruitful live discussions. Quite unusual was also the Art and Science contest,
organized by the ECCOMAS young investigators, consisting in a virtual exposition hall.
A virtual conference has its share of successes and sometimes small disappointments.
In any case, it allows to learn a lot about this new format for a conference of this size.
It is obvious that live participation is limited by the daily tasks of the participants. We
expected that the fact that the website remained open after the conference would
compensate for any loss of participants during the week of the conference, but the
numbers are a real message of encouragement with live participation that may haveexceeded 1000 in the plenaries and 400 in the semi-plenaries. This is the order ofmagnitude of a standard conference with, in addition, something that was appreciated:the exchanges through the chat and the discussion led by the chairs.
On the other hand, the researchers had the possibility to follow all the conferences thatinterested them, which is normally not possible when 50 sessions are held in parallel.
More than 400 presentations were viewed more than 20 times. Regarding the live
discussions on the mini-symposia, the participation ranged from 20 to 200 people and thepresence of experienced researchers in these moments of exchange was a great success.
Finally, more than 7000 messages were sent either in public (3000) or in private (4000).
These figures show that a virtual congress can nevertheless be a moment of exchange.
It goes without saying that direct contact with colleagues and friends is irreplaceable.
The proposed format was embraced by all the community to create a very special, enthusiastic
and successful scientific event. l
39 iacm expressions 49/21

Contributions from AMCA members to fight against COVID-19 in Argentina.
CFD helps to reduce COVID-19 transmission in massive transport 1
1

Damian Ramajo &Santiago Corzo
AMCA regular members
Centro de investigación
en métodos computacionales(CIMEC)–CONICET-UNL Santa Fe, Argentina

Figure 1:
Concentration of the
air exhaled by a infectedpassenger in the
subway wagon.
Effect of the HVAC on
virus spread. Concentrationof virus at different zones

OVID-19 is the worse pandemic of
XXIst century. At least two factors
promoted the fast spread of the disease:firstly, the high connection of people
around the world. Secondly, the people
living in crowded big cities. Massive
transport media have played a significant
role in virus transmission. However,recent numerical and experimental studies
have shown that travel by airplane should
be safe even for long time trips because
the laminar descending airflow as wellas the use of High Efficient Particulate
Arresting (HEPA) filters lead to adequate
renewal air ratios. These technicalfeatures added to strict boarding andon-board protocols seem to be enoughto reduce the risk of airborne virustransmission.

C

On the other hand, the scenario is completely different in urban buses, trains
and subways, where the average trip isrelatively short, but the distance between
passengers is lower, the Heat, Ventilation,
Air Conditioning systems (HVAC) recirculate most of the cabin air, and filters are
ineffective to retain viruses. One positive
and, three negative factors that clearly
increase the transmission risk. The bestsolution seems to be to enhance the air
renewal ratio by re designing the HVAC
systems, loosing energy efficiency, but
improving safety conditions. Although it
Exhaled air concentration

Time 400s
Virus concentration q/m3

Passenger in the left end of wagon
Passenger in the middle of wagon
Passenger in the right end of wagon
Analytical model
Analytical model without virus decay

iacm expressions 49/21 40

is obvious the role of HVAC on virus dissemination, it is also clear that the local
virus concentration around the infectedpassenger will be reduced by spread it
along all the room. Taking into accountthat a certain dose should be inhaled tocontract disease, it could be benefit forpassengers close to the infected.
Since mid 2020 we have performed CFD
simulations with OpenFOAM to give
guidelines for improving the safety intransport media. Figure 1 displays thesimulation of virus transmission in the
Buenos Aires city subway. The air
exhaled by a passenger in the central
zone is quickly aspired by the outlet air
vent and disseminated covering more thana half of the wagon after 400 s. That is,around 0.1% of the indoor air comes fromthe infected passenger.
Simple transmission models allows us toestimate the virus concentration considering perfect mixing assumptions. Thesemodels give us the average virus concentration, which is in general in a good
agreement with CFD results. But themain advantage of CFD is the capacity tovisualize the local concentrations, which
can be two or three times higher than the
average ones. Figure 1 on the bottom
right displays the average, and local virus
concentration in the wagon. As noted, thecentral zones (zones 2, 3 and 4) reach toconcentration values two times more than
the average. As shown, analytical models
are useful for preliminary estimations, but
the improvement on the current HVAC
systems should be supported by detailed
CFD modeling.
Cover to sneeze or cough. The wide
range of particle sizes exhaled by aperson during respiratory activities
represents a great challenge for the studyof disease transmission mechanisms.
The trajectory of ballistic droplets aftersneeze or cough is also affected by theindoor airflow. For large droplets larger
than 300 μm the droplets follow parabolic
trajectories based on the inertia force, but
most of droplets are smaller than 100 μm
and evaporate almost completely. As

for all inclusions under AMCA please contact
Pablo A. Kler
kler@cimec.santafe-conicet.gov.arshown in Figure 2, they are easily
dragged by the airflow, traveling morethan 4 m before deposition on the floor
and horizontal surfaces.
Remain silent. A speaking person
produces between 5 and 20 moreaerosols than a silent breathing person.
Figure 3 shows the exhaled air concentration caused by a person speaking forone minute. As noted, the HVAC systempromotes rapid spread of virus, but locallyreduces its concentration.
Opening the windows. The opening thewindows in urban buses is the best choicein terms of air renewal. Figure 4 displays
the outflow and inflow CFD results for abus moving at 40 km/h. The pressure
drop at the external front part of the bus
promotes high incoming flow through therear windows and outgoing flow throughthe front windows. As a consequence, 7s,
12 s and 26 s are needed to replace theindoor air at 20 km/h, 40 km/h and 60
km/h, respectively. These times are very
promising if we consider that a typical
HVAC system with a renewal ratio of 25%
it needs more than 235 s to achieve that.
Conclusions. CFD
simulation is a very powerful tool forunderstanding and improving
the safety of massive
transport media. Theenergy efficiency andcomfort but also thehealth concerns should
be included in the designof modern buses, trains
and subways. Meanwhile,
the opening of windows,
the maximization of therenewal ratio of HVAC
systems and the inclusionof efficient filters is
mandatory. l

Figure 2:
Spread of droplets aftersneezing of a passenger
placed in different zones of
the urban bus

MECOM 2021
XXXVII Argentine Congress on Computational Mechanics
Resistencia, Chaco, Argentina.
Dear AMCA Members,
We like to inform you that the thirty-seventh edition of the Argentine Congress ofComputational Mechanics will be celebrated from 1st to 5th November 2021, fully online.
We invite you to join us once again in a new edition of our Association Congress. You
could check for information updates in the website https://amcaonline.org.ar/mecom2021.
Best regards,
Dr. Ing. Javier Mroginski
Organizing Committee President

Dr. Ing. Guillermo Castro
Scientific Committee President
41 iacm expressions 49/21

conference diary planner
1 - 5 Nov 2021 MECOM 2021 - XXXVII Argentine Congress on Computational Mechanics
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Chaco, Argentina

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https://amcaonline.org.ar/mecom2021

10 - 12 Jan 2022 The Role of Mathematical and Computational Modeling in Cancer Research
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4 - 6 April 2022 EMMC – 18th European Mechanics of Materials Conference
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20 - 22 April 2022 2022 UKACM Annual Conference
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6 - 8 June 2022 CSME 2022 - The Canadian Society for Mechanical Engineering International Congress
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4 - 8 July 2022 11th European Solid Mechanics Conference
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31 Jul - 5 Aug 2022 WCCM-APCOM 2022 - 15th World Congress on Computational Mechanics &8th Asian Pacific Congress on Computational Mechanics
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18 - 19 Aug 2022 MLIP - Uncertainty Quantification for Machine Learning Integrated Physics Modeling
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September 2022 AIMETA - Italian Association of Theoretical and Applied Mechanics
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25 - 27 Sept 2022 MFEM2020 - Meshfree and Novel Finite Elements with Applications
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6 - 9 Nov 2022 IGA2022 - USACM Thematic Conference - Isogeometric Analysis
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25 - 29 July 2021 USNCCM16 - 16th U.S. National Congress on Computational Mechanics
Venue:

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6 Dec 2022 11th JACM Computational mechanics Symposium
Venue:

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23-27 July 2023 17th U. S. National Congress on Computational Mechanics
Venue:

Albuquerque, New Mexico, USA Contact:

17.usnccm.org

21 - 26 July 2024 WCCM/PANACM - 16th World Congress on Com. Mechanics & 4th PANACM Congress
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7 - 10 Dec 2025 APCOM 2025 - The 9th Asian Pacific Congress on Computational Mechanics
Venue:

iacm expressions 26/10 42

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