Welcome. Today is Monday, June 12, 2017 and this is the Natural Hazards Engineering 101 Webinar Series. It is intended to

provide a common knowledge base for the NHERI community for each of the primary

natural hazards in the NHERI Program. Webinars in this series, provide an

introduction to fundamental concepts and an overview of experimental and

simulation based research. Webinars will also provide an introduction to

numerical methods and computational tools used in the Natural Hazards

Engineering Research. For more information, visit the NHERI website at

designsafe-ci.org, where you can find links to the SimCenter and the NHERI

Learning Center. Today’s webinar is coordinated by the Natural Hazards

Engineering Research Infrastructures Simulation and Computational Modeling

Center. This webinar is supported by the National Science Foundation under Awards

1612843 and 1520817. Any statements in this webinar are those of the presenter and do not necessarily

represent the views of the National Science Foundation. Today’s presentation

is by Professor Ahsan Kareem from the University of Notre Dame.

Professor Kareem is the Robert M. Moran Professor of Engineering and the

Director of the Natural Hazards or NatHaz Modeling Laboratory at the

University of Notre Dame. He is also Co-Principal Investigator

of the NHERI SimCenter. His work focuses on characterization and

formulation of dynamic load effects due to wind, waves and earthquakes on tall

buildings, long span bridges, offshore structures and other structures via

fundamental, experimental, laboratory and full-scale measurements,

utilizing cyber and cyber-physical infrastructure. We are pleased to have Professor Kareem. providing the Overview of Computational Fluid Dynamics in Wind Engineering and I invite Professor Kareem to begin. Good morning and good afternoon

depending on which part of the country you are from. I show that some of you may

have listened to the two earlier webinars we had. The first one dealt with the simple

introduction to the wind field characteristics, and the second one was

the wind tunnel modeling of wind effects on structures, and this one will give you

an overview of computational fluid dynamics, simulation and computational

tools in wind engineering. All of us, if you recall that our first introduction to

wind was in our physics class in high school or somewhere where we saw the

Tacoma Narrows Bridge undergoing these twisting motions and eventually

collapsing. So here are some shots of similar things done digitally using

computational fluid dynamics. This is something dealing with the past and we

are trying to use modern tools, trying to reproduce what happened during that

unfortunate day. At the same time, if we look at the recent report from NSF

about human technology frontiers shaping the future, as you can see, everything is

digital. So we’ve got to start looking at the whole computational and digital

methods of finding wind loads on structures in order to take maximum

advantages of all the tools which are available with that. So this

particular talk will give some background and then basically I’ll be

dealing with computational fluid dynamics, NHERI SimCenter and Designsafe Center a Guided Tour, a CFD Case Studies, CFD Uncertainty Quantification, a little bit about

Virtual Wind Tunnel and something, if time permits, on Stochastic

Simulations. If you look at the historical perspective starting from Da Vinci.

Leonardo Da Vinci talked about turbulence and the issues with the big

eddies and the small eddies and then more recently a British meteorologist Richardson said that “big whorls have little whorls that feed on their velocity and little whorls

have lesser whorls and so on to viscosity.” So this is the dilemma

we face when we’re dealing with turbulent flows. That it’s a multi-scale phenomena

as we’ll see in this particular slide. Challenges and goals. We

have general circulation around the globe. Then we’ve got secondary circulation

and local winds, like tornadoes or hurricanes, downbursts. All these are local

events. But if you look in the, how the energy of these fluctuations is

distributed. This is the famous Van der Hoven Spectrum so most of you will

be familiar with this one and it is a low frequency with an average wind, and

this is the part normally which you call turbulence. This turbulence interacts

with all of these structures. We have bridge systems, we have tall buildings, we have low-rise

buildings, transmission line towers photovoltaic cells, wind turbines and

offshore platforms. And they create forces of quite complex nature, but

mathematically they are currently intractable. So in order to attack these

problems, NSF recently, a lot of you are aware, that NHERI Initiative has

a NHERI Center for Computational Modeling and Simulation of the Effects of Natural

Hazards on the Built Environment, and it is these are the Co-PIs and it’s being

hosted by four universities and many other senior personnel from many other

universities participated in that one. At the same time, while we produce software, that

software has to be delivered to people and the data analysis. There’s another

center, Cyber Infrastructure Center and Designsafe-ci at the University of Texas.

Again, these are the PIs involved with that and A Cyber Infrastructure for Data

Sharing and Analysis in Natural Hazards. So these are the, here’s the mechanism,

SimCenter for developing software and Design Center, besides other

assignments they have, they will be providing the tools we develop to the

community. So let’s look at, because you know people are always skeptical about

simulation, and about the experimental versus simulations. So the comparison. CFD gives an

insight to the flow patterns that are difficult, expensive or impossible to study

using experimental techniques. In the case of experiments, those of you have done them, you

know that it depends on the number of the data points you can collect, how many

sensors you have. At the same time, if you put a sensor in the flow, you disturb

the flow. So it all depends on the type of sensors you have, how many you have. You can

obtain the data in your domain whichever you are testing. At the same time,

CFD provides a very high resolution in both space and time, and it can be used

for virtually any problem and the realistic operating conditions. Of course,

there are always errors in both sides, and one of the errors is the sensors

themselves have frequency response functions that may not be able to pick up

some of the fluctuations which are happening, and at the same time the probes

could disturb the flow, as I said earlier. In case of simulation, we’ve got even bigger

issues. The modeling, discretization of our continuum space into a discretized

space, and then implementation of various numerical techniques which we will be

using. So CFD has challenges and perception also. You know, people mostly

the naysayers, say “Oh my God! Oh it doesn’t work. It is also not reliable.” There is a

reason behind that. I’m going to compare two areas Aeronautics and Aerospace and

Structural Engineering and see why this area has been so successful and why we

are still in the developing status on this side. CFD has a long history of

applications and it’s very mature in aeronautical engineering or aerospace. In

the case of structural wind engineering, it’s a short history of CFD applications

and still an evolving field. In the case of aeronautics, users are well versed in

fluid mechanics and numerical methods, while, in our case, limited background of

fluid mechanics and numerical methods. Users developed codes for specific

applications, in the case of structural engineering, we rely on commercial

software which is very general and with a broader application, and may not be that accurate

for one particular application we are trying to use. But these guys have a

bigger advantage. They have a very restricted type of flows and very simple.

We are stuck with a difficult hand here, because we deal with a broader range of

flows with a high degree of complexity, and turbulence on top of that makes life

very complicated. If down the road, we have to go from experiments to

computations, we have to make sure that the computations can provide accurate

results. Without that, it will be a difficult transition. Summarizing again,

advances in CFD with application to wind and its effects, are critical in enhancing

the performance of the built environment. Currently, we have wind tunnels and also

vortical flow facilities which can provide measures to assess the

performance of structures on the wind, but they also have some issues with

scale effects. You know, you have to use a model scale structure and when

whenever you are dealing with a nonlinear system these scalings can become difficult. Yet,

CFD offers, in principle, a most effective means of overcoming some of the

challenges and when combined with advanced data analytics approaches, has the promise

to provide data with great details that are not possible with limited

accessibility of sensors in experiments. But again, as I said, we have major

challenges because we have massively separated flows around structures

compounded by multi-scale fluctuations, and that’s why we have now the SimCenter

and Designsafe working hand-in-hand in order to come up, build a

large community of users so that we can share new advances in this particular

area. Just a little of 101 on what happens, what is multi-scale? If you

look at the fluctuations of wind, this is the spectral density function. As you can

see, bigger eddies and they keep on merging become small until they become so small

that they are just taken away, eaten away by viscosity and they disappear.

But all these scales interact with each other, and in the dynamics of the flow in

a nonlinear fashion which we will see in a minute. Here I want to show you a plan

view of a building and the wind is coming from the left and these green blobs

are the turbulence, but as you can see, due to flow separation and the wave

development, we have three regions not developed yet. This green one,

we have fluctuations in the red one, and the fluctuation to the brown one, and we have

different spectra for each. So we started with this one, but we had additional

spectra resulting from the fluid structure interaction which makes our

life complicated. If I go into 3D, here is a building and these ghosts

which are coming, these are like big eddies. In 3D, they wrap around the building, they

tilt, they twist, they stretch. They are all mathematically describable in some way,

but for simpler types of flows. And as you can see, there’s a big mass which causes

loads on the building. So in order to capture that, we have difficulty. So let’s

go to some simple case which is Aerofoil and as you can see, the flow stays

separated attached to it throughout which makes the life of the aerospace guys

quite easy. Let’s go to a little bit more

complicated body which is a simple cylander. As you can see, we’ve got big

[volumes] behind them and we didn’t see those in that case. So the problem

becomes more difficult to handle. And here comes structural engineer [to chart these bodies] and we have not only issues there, but we have issues on the side

of this as well. As all of you know, the flows are determined by

Navier Stokes Equations, and as you can see, here is a “u” and here’s a “u”. Two nonlinear [?] nonlinear interactions and that is why things become more

complicated in solving these types of problems. Just to give you a little flavor, after

seeing an equation, I think, it’s good to see some [?] here. Here is the — you’ll hear a lot about LES and RANS today. Here is a flow around buildings.

Here it is without any inflow turbulence. In this case, you have turbulence in the flow

and that changes everything in this particular case.

For those of you doing fundamental research, here is a long rectangular body and

a stationary body. In our research, sometimes you have to use force oscillations to

see what happens with the flow around the structure and the third option is

you have an aeroelastic structure which is more like freely oscillating and as you

can see what happens to this one. And as you can see, with this type of motion the

flow rate has become a lot more complicated. In this particular one, it is a

very friendly vortex [escarpment], vortex [shedding] behind a circular cylinder.

So let’s talk about what is CFD. It is an interdisciplinary area where you have to

know fields, numerical methods, fluid mechanics, IT some computer issues and

turbulence. So with these three things, we can understand and try to model things

computationally, flows around structures. If you look at this

particular diagram here, here are so many eddies, and if I want to resolve all of

them, I need to have a very fine grid. And when you have a very fine grid, that means

you need tremendous amount of computer resources. So based on what we want to

capture, there are methods developed which are, they’re able to capture and

I’ll begin quickly and then we’ll go back to the details of each. One is DNS

which is Direct Solution of the [modern] Navier Stokes-Equations. So the grid size,

in this case, is so small that we can only do some very basic problems and

that even takes a very, very long time. Then we have a large Eddy Simulation

which is all our hopes are on this particular method, and that is we move

into here and we only capture these blue ones and leave those other ones

unresolved, and now [they] use a mathematical model to try to

capture those. And the third one, which is most widely used, is RANS. It’s Reynolds

Averaged Navier Stokes Equation which averages out all the fluctuations and

makes life a little bit easier. But for structural engineering applications, it

has other limited application because it cannot capture already excessively

separated flow conditions. Let’s talk about that this DNS. Just to give you an

idea, the number of cells you need is this power to Reynolds number. So with these three

typical problems like the Space Shuttle you need 5.6 quadrillion cells

which are currently not possible for this type of problem to be solved

computationally. Large Eddy Simulation as I’ve already given you some introduction.

Here actual flow, we try to filter out these small eddies and only try to

solve these bigger eddies which are bigger than the size of my grid. And the

ones which are smaller than the size of my grid, we call them subgrid-scale and

we use some sort of a model. And there are many models. I’ve only listed a few

here which can be used. So there’s some part of the flow is captured. They are

resolved — that’s the technical word, and the [sum] part is modeled. We don’t

analyze it computationally. Here is another description of the same thing.

Bigger eddies and smaller eddies. Physically speaking, here are the big

eddies — it becomes smaller and smaller and smaller, and those of you who like

spectral descriptions of these, here is the spectra and this is the low

frequency part which we like to computationally solve or resolve it, and

this is the one we leave for modelling it. The last one is the Reynolds-Averaged

[…]. You to take care of all the fluctuations. But once you do Reynolds

Averaging of the Navier Stokes Equations, new terms results which are

Reynolds stresses and it is the modeling of the Reynolds stresses which is the

primary function of RANS model and I’ve listed also a few of those here and

there are many more available in the literature. Just to see, from a spectral

description point of view, if the fluctuations[ in my] flow are decided

by this spectra, I do not resolve anything. You see, there is nothing here.

Everything is modeled. Then we have LES. The same thing. This is

what I need to model. You cut it off and you resolve this part. And this part, you

model it. So that is the basic concept behind these techniques. So let’s put

everything together on one diagram, and here we have a flow around a body. You

could either use Large Eddy Simulation, but you could use RANS here. So the

problem here is you [have] sharp edges separated flow features. You can see all

these eddies which are forming. Sorry. And if I look at slides of that here, because

the wind coming, we’ve got a separated zone, reattachment, wake. So all these things

are creating a complex situation for us. So we need to have this thing to be solved

computationally. We need to divide this whole domain into a grid. Then we have to

also worry about near the wall what happens, and there are, you know, you can have

a complete course on this particular area of study. At the same time, we have this

complete domain. We have inflow conditions. Something has to go

out also, and there are lateral boundary conditions. You don’t want to make them

too close to the body. This is our body. Just like in the wind tunnel, you don’t

want to have, constrain, the flow by keeping [Godric on the blockage] effect. Just to come back to these [cartoons]

again, if you look at [Airfoil?/Aerofoil?], depending on the angle of attack, its bluffness

increases and, you can see for a larger angle of attack, it also becomes like our

body, and we see there is a separation here. So on this, I am showing you the

bluffness going on this direction with the angle of attack in this case, and the

vertical axis tells you the model fidelity — how good the model has to be.

Ideally one would like DNS, but I think LES for this type of flow. In the middle

can we live with RANS, and in this case we can do laminar solutions. Something

more closer to us, is our circular cylinder, and here we can see with

the increase of Reynolds number, the flow features around this change, and

they make life — make this particular body more complicated, especially when we are

in the supercritical range, you have to use LES. This type of modeling would not

be very fruitful. Just again, just to give you an idea of [all thought or that] this

is another one URANS which I didn’t mention. This is a [finer] version of

RANS in which some fluctuation at low, very low frequencies are captured.

But this is LES, this is LES with the [In…and flow] conditions, and once again

all these things we have discussed so many times. You resolve certain parts

of your model, some part, and in case of RANS, you model everything. So the

quality of your results very much depends on the model, which is good.

Unfortunately, what happens is, the different regions of the flow are better

to model by one particular model, and some of the regions are better

represented by another model, which makes things a little bit more complex.

Okay, that’s CFD 101 again. We have got a problem statement, a mathematical model.

In this case, we have Navier Stokes Equations. We have to generate mesh. We

have to discretize space discretization, time discretization, iterative solver,

simulation runs, post-processing, and of course, we should not forget, that’s where

the problems are. If we don’t verify and validate our results, the numbers

won’t mean much. At the same time, we have uncertainty quantification. Just

like when we do experiments, we put some bars which shows the confidence we have

in our numbers, because we run experiments several times and then we

try to determine [that path]. But in case of CFD, we have to do the same thing

and [what we call] uncertainty quantification. Uncertain parameters, their

uncertainties propagated through the analysis and we will chat a little bit about this

[in the end]. So there, there are a lot of advances in mesh generation.

People in computer science, computer and computational methods study this.

But basically, there are two types of generation of meshes: structured or unstructured. And we can use CAD tools these days and

great generators. All sorts of things can happen. We don’t have to do

it the same old way like many, many years ago. People used to that. At the same time, we

have to use space discretization. That means spatial derivatives have to be

approximated, so they can be done by finite difference method. You know

whenever we take a simple loss in numerical methods, that is the first one

method we use. Or we use finite element method. But for fluids, it’s better that

you find a volume method is used which helps us in better capturing the flow

field. Time discretization again is very important. I think you might have heard

if you’re not doing much of computational method, explicit method,

implicit scheme. You have to worry about the stability, local time-stepping is

also very important with the quality of the solution which might be what you’re

looking for. What are the commercial softwares available? I think there are many

of you who use ANSYS Fluent and ANSYS CFX or CD. There are many others but

our focus is going to be on OpenFOAM because you [could] develop all tools in

an open source code which helps us to — we have access to the core, so we can

manipulate it. In all these cases, they’re like black boxes. You can just give input

depending on the instructions you receive from there. How we divide the

computational domain. For example, if you have unlimited space, and a plane is

going by, we need to make this domain capture that domain, and how do we do

that? The best way to do it — this a good example, by looking at the flow around

the plane, we want to capture these flow lines and if you take a very truncated

version of the space, you know these red portion is not truncated, and that

would not be very good for your results. And I think these are little things

which happen which keep on adding to final results and the unfortunate

mislabeling that the CFD is not reliable. Mesh generation. Let’s assume that this is

the domain we have. We generate a mesh. Mesh is very important. Rate of convergence,

accuracy of the solution and the CPU time required for that one.

And there are of course, there are many other criterias which help us to determine

which is a good quality. As I said before, we have a structured mesh, unstructured

mesh, and if these are the bodies we want to model, these are the differences. You

can see that certain mesh cannot follow everything as smoothly as compared to

an unstructured mesh. Things move, in case of wind engineering, so the trouble is the

mesh has to move also. So things get even one notch more complicated and we have

to use dynamic mesh. Here is an example of a circular cylinder in which it moves

and so does the mesh. And here is the case of a rectangular cylinder for the

same purpose. Then we have to worry about inflow conditions and outflow conditions.

It’s a little complicated. Let’s go through this. Like in the wind tunnel, we have a

long [fit] of part of the wind tunnel where we put [surface] softness, try to

generate flow which can mimic the flows for an open country or an

urban situation or [ in so local ]. Same thing is done in fluids, that you made this —

you let fluid go through a certain region and keep on recirculating it and

rescaling them, and once it is ready for being used you let it release into the

system. That’s one approach. The other approach is, people say why don’t

you do stochastic simulation of turbulence. But the trouble is stochastic

simulation does not obey the laws of fluids because it doesn’t know fluid is

[nor that] and once you enforce something which does not follow fluid mechanics, like

[continually patient in a sector], the problem just could start from the wrong

foot. So there are specialized techniques which help us to develop inflow

conditions into the domain of analysis. Just like a wind tunnel, your boxes, you

stack them up in sequence and we can have that talk shortly. Here are are some

examples from Professor [Tasura Tamura] from Tokyo who

has done quite large simulation of like a little urban development here,

and then you can zoom on the areas you would like to see, and as

you can see, flow around this particular building in two different levels is also

obvious from there. Then comes, you know, these are only isolated buildings. If you want to have regions, then we have to use WRF, Weather Research Forecasting

model, which sometimes is also coupled with LES, or nesting we call it, and that’s

how we can model large-scale weather systems and also embed them into our local

simulation. For example, it’s a very large domain, you keep on zooming until you

get into the area where is that of interest. You start with those inflow

conditions and then you nest it with your regular LES type of solution. So

computing time for flow simulation depends on a number of reasons.

The numerical algorithms you’re using, linear algebra, stopping criteria,

discretization parameters and the quality of simulation depends on the mathematical model and

underlying assumptions. The more assumption we’ll make, we’ll be

compromising some of the quality of the results. Stability, mesh, time-step,

error indicators, stopping criteria — all these things you have to embed into your code in

order to come up with some reliable simulations. What happens when you’re

interested in fluid-structure interaction? Well, you know, the vortex

induced vibration, the buffeting and the flutter takes place. Now the body’s going

to move, in this case. So we have to use a Transient Flow Solver which, again in OpenFOAM

the open source code we have as an option, we can use either pisoFoam or

pimpleFoam, and at the same time, you have to have a Structural Dynamic Solver

which could be a Newmark-Beta-based or state space-based method. We have to have

a Dynamic Mesh. They all interact together, and we get applications which

lead to vortex shedding, vortex induced vibration galloping and other issues. Now

I won’t go into the detail of this detail. I just want to make you aware so

that you can look for when you work in this area. The coupling scheme, there are two:

either Fully Coupled which many of us don’t use it, but the Iterative Coupling

or Sequential or Partitioned one we use, and in that case, the

trouble is we use Fully coupled then both of these solids or the structural and

the fluid model have to move on the same [time] which makes life quite more

complicated. But if you do interactively — here’s an example of Fluid Structure

Interaction in OpenFOAM. It’s a Partitioned strategy, and in this

case we have Fluid domain and here is the Structural domain. You could have a

Structural program like OpenSees or some others, and here is your OpenFOAM here

and this interacts through this interface. Where, for every time step, we

have to deform the mesh and then feed it back into there. And things get

a little more complicated when we are dealing with FSI type problems.

Post-processing. You know once we are done with that, we need to calculate many

other derived quantities some integral parameters because, if you have a local

pressure fluctuation, you may like to find the level load or the base shear,

the base bending moment. We may like local zooming which is not possible

in wind tunnels, flow visualization, and we have to use statistical tools to

systematically analyze the data and of course debugging, verification, validation

is a very fundamental thing. Here is one [cartoon] which we use for wind pressure

fluctuations on a tall building. The data would be coming from CFD or it could be

coming from a wind tunnel result. The same software is going to be used which is

available now on Designsafe where we can have an OpenSees or any other

commercial package trying to solve the dynamics of this building and the

pressure fluctuations coming from either a wind tunnel NHERI EF site or could be

from CFD could be fed into that and you could be getting other, you know, various

results of various kinds — accelerations, bending moments, equivalent static loads,

and the power spectral density functions. It’s a lot of processing and this

particular software will help people to do that rather than everybody

reinventing the wheel, in this particular case. We are already going, hopefully in five years,

we are going from isolated clusters to communities, like, for example, this is one

building in Houston we tested [upper error] again.

From isolated buildings, we like to go to clusters and we like to go to cityscapes

where we can have, [see] flow around the whole city. Many of you taken undergraduate

fluid mechanics classes, we use flow visualization with smoke, and then try

to capture that smoke with the camera or our naked eye. I think down the road, and as even

now, we are having this digital flow visualization. As you can see in this

particular diagram, where all the conical vortices, the horseshoe and the conical

vortex at the top can be captured digitally by releasing, or following a

particular particle, like it is shown in this particular cartoon as well.

Verification and validation is very important. Without that, we cannot trust

these values, and I think here is an example again from our Japanese

colleagues. They have done something in the the wind tunnel and then they developed a CFD

model of that one. They had the luxury of 1 billion Mesh Cells, 6,000 more than

6000 Parallel Cores on their famous Japanese K Computer which they developed

this virtual wind tunnel, and they tried to compare those. And, as you can see, without

going into every detail here, you can see the comparison between LES and experiments are

pretty good — a dashed line and solid line. And here is LES versus the experiment for

the maximum values and this is same thing for the minimum values. So for that

reason the Japanese Building Code or Standard now allows designers to use CFD

to design their buildings, but there is a caveat there. It says it has to be done

under the supervision of an expert which is I think a fundamentally

important condition. What we have done here is that, if for those of you are not

familiar with OpenFOAM, it’s very easy to download all the instructions. It’s an

open format, everything is free. So Stampede is a large computer, one of the largest in

the United States which is at TACC at the University of Texas under the Designsafe. You, as a user, could directly go and use

OpenFOAM and use the TACC resources. And here are some of the pre-processing you

have to do. All these — system, constant and polyMesh — are the fundamentals which are

required when you are trying to use OpenFOAM. Once you have that, you have to

load your things on one of the directories which is available. Then you

log in nodes in a batch mode and then then you let the computer nodes solve that, and

and like this one, we were lucky here. We are having all the results which are

possible, and this is a great computational resource available. For

those people who are not at that level yet, to be directly going on to Stampede,

we prefer that they go through the Designsafe system where you have to —

here’s where you are, with the OpenFoam values you have on your system, then

there is a Data Depot. So you transfer all your information into

various files here, as you can see the name constant and the system is here. These files

are stored here. Then there’s a workspace, Discovery Workspace in Designsafe, where

you select various solvers and all that and the program runs and your end result

is thrown back into the Data Depot and you can then suck it back into your system

[and try to ? it]. This is a little bit more friendly and more organized, and I think

most of the beginners and average users should be using this particular form.

All you have to do is go to Designedsafe-ci.org and you can, if you don’t have an account,

you can open up an account and then you are free and ready to go.

As you can see, OpenFOAM is up there in the system. It’s all loaded, you don’t

have to do anything up there. So at this time, you have these three

things on, we are delivering on Designsafe. This OpenFOAM through the Designsafe-ci,

or you can go directly to the Stampede, or we have also a Vortex-Winds

Module has been loaded which provides you aerodynamic loads on buildings of

various shapes. There are several databases that have been merged together behind the scene.

You don’t have to worry about it. You pick up a particular geometry,

and you will get those results. [Tokems?] is another issue. This is more

like a new developments. The question is that we have already discussed so many times

that RANS is on the low end of fidelity of the quality of results. DNS is

way on the top and in-between is LES. A famous professor, late professor Ferziger —

we benefited personally from him he was a professor at Stanford, one of the

leaders in CFD. He said, “if it turns out that LES can be

done on very coarse grids, it will be one of the few times the nature has been

kind to us with regard to turbulent flows.” So the best thing is that even if

you want to use LES, it is computationally very demanding. So

the issue here is, which I’ve been thinking, that if we develop some sort of a transfer function between the result some RANS and LES

for a class of problems, like in this particular case, the RANS is a low

fidelity simulation, it’s computationally very efficient, so that’s why we love it.

But it has a large modeling error. At the same time, LES is very computationally demanding

but has higher accuracy. So if we develop a meta-model which relates these two, we

can be able to run RANS, but get the quality of LES. I think this is going

to be some of the future where model fidelity can be taken advantage of. Here

is some example. We have utilized it already in one of our previous NSF

projects where we are looking at shape optimization of buildings. So we

start with the number of initial cases, and the trouble is you have to run

simulation for each case. We decided that we develop a meta-model or a surrogate

model which relates RANS to LES. What we did — we ran many RANS

and some, the purple or magenta ones, are the LES. Based on that, through

[Craig Ing], we could develop a model. And here is that model surface, where

we run RANS, it comes in there and the output is given is LES. So for a

certain class of problems, and one can develop such things for many classes of

problems, and eventually through machine learning they could cover the entire

domain of computational fluid dynamics. That’s down the road, but there’s a good

initial success, in this particular case. If somebody wants to do real, deep

research, for example, we did several years ago with 45 million mesh cells. You

can really look at the nitty-gritty of flow around these bodies, coherent

structures, or you can even cut slices and look at the structure of flow around

the body. But this is for those people who are really interested in the fluid

dynamics of fluid structure interaction issues. Now comes the question of

uncertainty quantification in CFD. There is, you know, most of you are familiar

with aleatory variability which is natural randomness in the process, and we

cannot control it and one of them is an inflow free stream turbulence and its

characteristics. The other one is Epistemic which one is a Model-form

uncertainty and this is the discrepancy between the model and the physical

reality due to lack of information or knowledge. So our job is that we have to

propagate these uncertainties in Inflow, in Boundary conditions, in Initial

conditions, in Model uncertainty so that they enter our numerical

simulation and are present in our solution. There are various methods. Some

are very sophisticated model Intrusive methods. They are not possible yet for turbulent

flows where [interacting with a body], but for simple types of problems, they have been used.

The trouble is your deterministic problem is recast into a stochastic

version in order to analyze and one of them is a polynomial chaos, but there are

Non-Intrusive methods, and this is the very simple method, keep on running, it’s

Monte Carlo simulation, but in case of throwing a dice or a coin it’s very easy

to do it 1000 times, but if I have to run a CFD 1000 times,

that’s very demanding and challenging. So there are other ways to overcome this

difficulty. One of them we call it as a stochastic emulation. One way is that we

develope a surrogate or some surface which helps us to determine for infinite

number of inputs very quickly we can get very fine number of results which can be

then statistically analyzed in order to get and quantify the uncertainty. Dakota

is again open source code available from Sandia Laboratories. It’s

available on the net. It has all these methods embedded and there and one can

connect and link them and and also we are linking them with OpenFOAM which

will help us to do some of these things which I’m saying here without actually

running into the programming effort of all those. Here is an example. We did the —

let’s do using RANS because that’s easier and faster to run, even that took a lot

of time. So we use turbulence intensity and length scale as our two uncertain

parameters and we wanted to see how they effect our bluff body. Again, we are doing

shape optimization with this stochastic emulation. We’ve got length scale, and [?] — two variables. And then, we have two other variables which help us

to change the shape of the building. So we go through this whole surrogate

calibration and in the end we [resolve] this surrogate model here, or response

surface, if you wanna call it. This is for the Mean solution, this for the Mean plus

one Standard, and this is for Most probable solution, and then we do the

optimization and the end result is the various Pareto fronts where these are our

optimal values. If we are interested only in Mean solution, we can stop here. If you

want to go this one plus Standard deviation that’s here, or if you want to

go for the most probable solution. So that’s how the uncertainty which began

into our system can be eventually propagated and quantified in the end in

regarding our results, and in this case, this is the left coefficient and this

the drag coefficient, and these are the optimal shapes for those particular

quantities of interest. A little bit about Virtural Wind Tunnel. Several years ago, we

started this small pilot project with developing in a prototype scale. It’s a combination of SketchUp, Facebook and OpenFOAM. It

provides — you can convert structural geometries to CFD cases. You can file share

[with] the community. If you have any trouble, you can send it to your colleague here.

[I’m going to trouble can you] look at it. Access to CFD tools. OpenFOAM is

embedded in here and analysis of results. So this is a unique tool for

establishing the Virtual Community of Users who can talk to each other and

share the results. Is the shared software, shared hardware, collective knowledge and

also crowd sourcing is becoming part of it. The various case management — you can

have different structures which you are picking up. When it’s running again you

also have some little gadgets, try to see what is happening with your simulation.

And the best thing about this one is the tutorials because you’ve got — everybody’s

not fully trained in the numerical methods and fluid mechanics, so we have

provided and we are developing still more tutorial dos and don’ts. And we

have been very successful through a crowdsourcing experiment, we found out

that people who have never heard the word CFD before, and have no knowledge of

engineering, they were able to come up for the right drag coefficient for the

Empire State Building. Then you can do a lot of post-processing.

Perot you can help you to do the flow visualizations on others. How good it is?

So we decided, you know validation is always important. The Virtual Wind Tunnel

— we went into the box, and circular cylinder, squares. These are our results,

these are some of the experimental results and you will agree with me that

these are pretty good. One of the best papers on flow around

square cylinders and its drag coefficient variations is Lee (1975)

and here is our [radiation] the blue one is the Virtual Wind Tunnel. Again, realizing

the inflow conditions being slightly different, it is still a very, very good

comparison with the results. We have done some more extensive validation and

verifications using NACA Airfoil using two models: SA – Spalart and Allmaras and SST. Those of you that have done these models are advanced models, and as you

can see, I want to show you that we did many verification studies and is

supported in this paper, but I just wanted to have you look at –this is experiment 1009100778 — is the lift coefficient with OpenFOAM,

and drag coefficient is almost the same in that particular case. So one has to

get involved and develop the culture of trying to verify some results and once

our software and the fluid schemes which we use are validated enough, then we can

go beyond and looking into more cases which we would like to analyze. So the

goal is eventually, the very [mild peer point] is that I’m sitting as a

researcher or a design engineer. I don’t need to go anywhere. I should be able to

do everything digitally. I should have damping database available

to me. I could have computational platform which we are just defining. Data-

enabled design, meteorological data. If I need to do a wind tunnel experiment, at one of the

EF sites, I could do it through tele-experimentation and one could have all

these resources coming into this particular gentleman or lady to

analyze a structural tall building or any low-rise building or any other

structure which is of interest. One should not forget about other

stochastic simulations because they also come in handy in analyzing and in modeling

some of these results we’ve obtained. For example, we have

model-based approaches to [drumming] loads. They could be random in time, frequency

or time frequency. We can use Volterra based models. They have the [second order

kernel]. Once you have these kernels, we can do a lot of things with them to find

the response of structures. Then there’s a data-driven — everything in this

world is not data-driven, AI-driven, so the same thing applies to our work also.

We can use data-driven models to present stationary, non stationary,

nonlinear systems. For example, here is a hysteresis loop. It’s not a structure

under seismic loading. This is a bridge with a different angle of attack, and then

you bring it back, it does not follow the same path because there’s a hysteretic effect,

and these things have to be modeled and one could always use machine

learning and artificial neural networks. So this — the stochastic end and the data

analytics end is very important. This is eventually coupled with our CFD things

to come up with some of the world answers to some of the problems we are

still facing. Then we have cyber-physical infrastructure. We have to do cross

platform validations. Computational [versus] EF sites. We can do Hybrid/Hardware in

the loop type of experiments in which certain things which we cannot

physically model in the wind tunnel or in the CFD domain, we can do that physically and

then provide input to it through the wind tunnel or a CFD experiment. We can do

conditional simulation based on measurements. For example, in a wind

tunnel I have about 10 [hot films] to give me velocity fluctuations

at the site. I could use that and stochastically simulate fluctuations

and wind moving the measured values at those particular locations. And at the

same time, we can drive actuation systems for shaking of a frame, or a cladding

system from either a wind tunnel or CFD [outposts]. Where we are I think, just to

summarize, for us, the SimCenter and the Designsafe portals. You have direct,

access, as I showed you, or we can use through their portal. I was looking at these

Multi-Fidelity Models which I think is going to be the future, combining

them with Machine Learning tools. OpenFOAM-Dakota linkage will happen and

we will have all these things [buried] into there. So every analysis they’ll do

will automatically also provide us uncertainty quantification.

Virtual Wind Tunnel — I talked more about the advantages of that. A CFD-based input

will go into performance based wind engineering, and of course all of these things

are being done in close collaboration with the two Centers. I think I wanted to

also say that you need to remove the fear out of CFD. Most people are afraid

of CFD and I think that should not happen, and for that reason I think the Virtual Wind Tunnel, although it’s sophisticated tool, yet it will also provide as a tool

to play on your iPad. Try to have different shapes and try to make changes

and to see what small changes you make how that affects the results. So I think

this would be a very good way of developing a large database, people

running their cases and trying to deposit them in a database, and as you

can see more recently there is a game Mozak, in which people — the crowd helps

the scientists to provide input for mapping the brain cells. It’s at

University of Washington. I think we need to have — we will like to have more digital

learning hubs, so that people are given short lectures on fluid mechanics and

the dos and don’ts of what we need in computational fluid dynamics. Of

course, there are a lot of chat rooms on CFD but most of them are not applicable to wind

type of problems because people are mostly doing aerospace research. So I think we

need to make chat rooms which are more usable for the wind engineering community,

developing collaborations, validation and benchmarking, and hybrid use of NHERI EFs

for cross-platform validation. And I think with this slide I would the end

with the vortex shedding in nature. These are the migrating whales, and as you can

see when they flip they create the vortex shedding, very similar to what

they see behind the circular cylinder. And this becomes a nice environmental

engineering problem as well. With that I would thank you for your attention and

would be happy to follow it up with questions either today or later.

My email is Kareem, my last name, @md.edu and we can have a dialogue,

because we want to develop, cultivate a community of users for these tools so

that we can advance our capability of analyzing structures exposed to natural

hazards. Thank you very much. Thank you Professor Kareem. Thank you for

the presentation and all the material. At this time, we will open the question and

answer session. Attendees are reminded that questions should be submitted

through the chat panel and sent to the moderator. We will relay these questions to

Professor Kareem this way. And so we do have a couple questions. Let’s start off

with wind tunnels. What role have wind tunnels played in the development

of computational fluid dynamics? As I said, wind tunnels are an essential tool

at this stage when we are developing this [scheme] for validation of our

simulations. So even in aerospace industry, although in certain areas, they

are not mature enough that they don’t use wind tunnels, but again when they are

looking at the maneuvering of aircraft they do CFD and then they go back into

the wind tunnel to develop more confidence in the robustness of their

computational simulations. So wind tunnels are still going to be an essential

component of wind engineering research. They’ll go at this stage

hand-in-hand, but eventually my goal is, and my vision is, that just like

aerospace engineering, CFD would go ahead and wind tunnels would be at the same time

only providing some validations. But how long is that going to take, I not sure.

Okay, along those lines, what are the advances needed in physical testing

to provide sufficient validation for CFD? Yeah, I think this is

some of the drawback, I mentioned. At this time, although we can measure

pressures at five, six -hundred [taps] on a building, but we cannot do the same thing

with the flow field around the structure. So if we put any sensor around the

building, that disturbs the flow itself. So what we are measuring is not the [nascent]

flow, but it’s more a disturbed flow. So I think we need to develop techniques.

I think [Laser Doppler Anemometry] is a very helpful, and more advances in those would

come hand-in-hand, and I think the more advanced sensors we have, and their

placement in the wind tunnel, the better it would be. Okay, moving outside of wind

tunnels, are there opportunities for improving models by using field data,

collected using accelerometers, placed on buildings, anemometers and pressure

gauges, or are wind tunnels providing the information we need? I think there have

been many studies about low-rise and high-rise that there are good

comparisons between what we measure in full-scale and then there are some

questions there also. I think mostly the low-rise buildings have more issues then

we have for the high-rise buildings, but I think full-scale, there [has] always

welcome and very useful and I think we cannot discard that, it’s a critical component

of validating the wind tunnel also. So all three things going hand-in-hand would

be a very useful trio. Okay, regarding inflow turbulence in CFD simulations.

There are a couple questions. Is it not true that the smallest scales tend to

dampen quickly and are less important than large scale inflow characteristics,

and what is the threshold in inflow turbulence scales in this regard?

Oh that’s a good question. The trouble is that if we do not model small scale, I mean

that becomes easier for our simulation, but the trouble is we are

losing the physics of that. As you have seen from our experiments, that the

small-scale flow scales double [in numbers] creates some of the modifications in the

flow around the structure, and if we omit modeling them, we will be seeing results

which are somewhat compromised. So one has to make sure that you can carry on

as far as you can, but again we cannot go the DNS, Direct Numerical Simulation.

So some place you’ll have to use some sort of model to take care of the smaller

eddies in there. Okay. Small scale turbulence can be generated within the

domain by flow obstacles such as buildings, vegetation. How far from the

inflow boundary do you need to be, such that the inflow turbulence may become

less important or how true is this? Anything which is in advance of the

structure is going to influence the flow field. You know there is one boundary

layer flow which is in the building codes — a very nice clean profile. But in

the real world, that clean profile is sort of an average quantity. If you have some

structures in front of your building, they are going to modify it. So it’s better

that one does a model — anything which comes in front of it or have some

indirect effect of that intuited in there. And I think, once again, there is no hard

and fast rule. We will sort of use the same strategy we used in wind tunnels that

that many blocks of the city has to be modeled in the advance of the structure.

Okay. How small are the meshes that we’re using currently to model

buildings? It depends on what — as I see that the Japanese had used 1 billion

cells for a typical of that city block, so that gives you an idea how many, what

is the size. Again, it will depend upon the nature the problem we’re

dealing with. What is the size of the domain you have, and there are many factors

which determine these numbers and [arons?] number also affects them.

And what is the role of the virtual wind tunnel in moving CFD forward to be

used in design practice? It is a more convenient tool for those people who

don’t have all the background, because it holds your hand and also tells you

you can go this way and not this way. And behind that is a very powerful OpenFOAM

simulation engine behind it. So we can do sophisticated analysis, but right now our

capabilities are limited. As the time goes, we will be going into more advanced

possibilities of structure. Our goal is that we could have a SketchUp, those

of you that you have used it. You can just sketch anything and make a building or

surroundings and they should be used in there. Right now we are still — we are

doing 2D only but very, by the end of this year, we’ll have 3D capabilities

with designed inflow conditions will be coming into the picture. Okay,

and in regard to computational fluid dynamics, what are the most obvious

questions that need attention? And by this, the question is what is the low-hanging

fruit for NSF grant seekers? Grand secrets? People writing proposals. What

are the questions related to CFD that need answering? Well I think primarily I’d

summarized in my lectures some of the the key nature of the thing. First thing is — what model

are you going to use and if you are going to use RANS, for example, you’ve got to

justify that that is good enough for the type of problems you have, and you can

justify your statements with some results which you have found in

literature where they are justified. Or if you want to use LES, I don’t

think you need any justification, but then I think you also have to provide

some justification. What type of subgrade scale model are you going to

use with that one? And what type of uncertainty you have to worry about. Then

grid generation — if it’s a static structure, if it’s dynamic structure, what type of

dynamic mesh schemes are you going to use? So there are a lot of things. If you

want to use CFD for your research, it has to be very well researched, in

order to have a good proposal. Okay. Well, we are at the conclusion of today’s Natural

Hazards Engineering 101 webinar. On behalf of the attendees, thank you

Professor Kareem for taking the time to share “State of the Simulation:

Practice in Wind Engineering”. To the attendees, thank you for your

participation and questions. The NHERI SimCenter is shifting the focus from

wind engineering to tsunami engineering for upcoming NHE 101 webinars. Register

for these on the Designsafe web site, and check your email inbox for emails

from [email protected] that will have registration links for these.

If you aren’t receiving these email announcements, check your Designsafe

account settings to include these types of announcements. Thank you for attending

today’s webinar. Thank you. you