Advancements for Simulations in Built Environment

RENI RAJU: Good morning, good afternoon, and good evening, everyone. Thanks for joining this class. My name is Reni Raju. I'm a Strategic Partnership Director with Ansy and along with me is Juliano Mologni. Juliano, you want to introduce yourself?

JULIANO MOLOGNI: Yes, sure. Absolutely. Thank you very much, Reni. Thanks, everyone, for joining us today. My name is Juliano Mologni. I'm the product manager for some of our electronics products here at Ansys. Basically focus on electromagnetic simulations, antennas, electronics, print circuit boards, ICS. And I'm based in Michigan on the United States. Back to you, Reni.

RENI RAJU: Thanks, Juliano. So today's class, we will talk about advanced simulation for built environment. This is primarily going to focus on using Ansys solutions to make buildings cheaper, safer, more comfortable and more environmental friendly. One of the assumptions that we are having today is that if you're joining this class that you might have no simulation experience or maybe some basic simulation experience. But we will introduce the concept and examples to highlight how high fidelity physics particularly can be used by the engineers to solve a range of problems in the built environment.

So a quick overview of the agenda. I will give an overview of Ansys for those who might not be familiar with who we are. We'll make some observations about the market drivers in the construction industry. We'll talk a little bit about the benefits of advanced simulation in the built environment. We'll lead with some examples. Obviously, it's not fully comprehensive, but we'll try to show some examples in the areas of fluids, structural mechanics, as well as electromagnetic simulations. And, finally, we will summarize our presentation.

So let me just introduce Ansys. We are a 50 plus year company. We have been doing innovation for many, many, many years now. We are definitely the number one in engineering simulation software. And we are powering innovation across every industry segments. This includes health, aerospace, and defense, automotive, et cetera. We have more than 6,000 employees within Ansys and we have more than 90 offices worldwide.

Our mission is primarily focusing on powering innovation that drives human advancement by closing the gap between design and reality using simulation. As you can see on the screen, we are definitely very cognizant of the transformation that's happening across multiple industries, be it smart cities, smart buildings, connected systems, sustainable aviation, and sustainability across all the industry segments, as well as the new industry trends like electrification and autonomy as well.

And together we are essentially trying to redefine simulation. Obviously Ansys from a product portfolio perspective consists of many core physics solvers. We'll talk about them a little bit more in detail during the presentation today. But beyond that, we also have system solutions, software solution, safety solutions, and mission engineering solutions. But our goal is not just to leverage our portfolio for doing simulation, we are also trying to create a platform which is more intelligent, open, and agile that enhances and extends and amplifies the value of Ansys portfolio.

We are obviously not doing this alone. We are trying to work with an ecosystem of partners over here. We are trying to work with startups, we are working with academics. But, also, we are working with strategic partners, for example, Autodesk, to create an ecosystem of development partners which enable unprecedented innovations.

So let's talk a little bit more about the drivers in the construction industry. Obviously, everybody is concerned about the rising cost of energy. If you are like me going to the gas station, I get a heartburn every time I see the increase in the gas prices. But beyond that, you're also looking at scenarios where there are multiple natural disasters, be it tsunami, earthquake, eruptions of volcano. And, especially, nobody thought five years ago that we will be coming to a time where we'll be living in a situation with COVID, wearing masks for two years.

Obviously, construction industry definitely is compliant with the building codes. The cost of construction is going up, especially in the time of inflation. But also emergence of new materials. I talked about sustainability in the previous slide. As you can imagine, new materials are being considered for new building design. How does it impact the performance and the stability of the building that needs to be designed? And finally, last but not the least, I would say that there is also the aspect of creative design. However, creative design sometimes can lead to the impact on the surrounding environment, occupant, and pedestrian comfort and safety, which needs to be accounted for.

So when it comes to pain and challenges, the first and foremost thing is always safety. The buildings that you guys are designing are subject to a range of challenges. Some could be small, some could be big, some could be made because of mistakes, and some could be natural as well. Case in point is earthquakes, tsunamis that are mentioned in the previous slide. Some of these safety concerns are only are seen on a daily basis. For example, you're having a building in a high headwind environment. Or sometimes it could be once in a lifetime, which could be a 500-year-old flood that might show up.

Sustainability is a key theme. Obviously, today, every single company out there is concerned about their ESG goals. How do you make a comfortable living conditions throughout the year while minimizing the energy consumptions while at the same time try to keep the cost at a minimum level?

And the third aspect of this is, as you think about more unconventional sometimes unpredictable technologies that fall under the comfort zone of many practicing engineers and architects, how do you account for that within your building design? So as one of the customers and one of the designers, you are primarily concerned with the cost, time, and competition, right, when it comes to the market.

So in response to that, obviously the civil engineering companies are focusing on many of these initiatives. You're looking at smart buildings, which could be, for example, stadiums which can be adjusted for different environmental activities. Passive safety is critical when it comes to people and ownership. Green building to make sure that these buildings are more comfortable for people. Obviously reducing the cost of construction and services. Building information modeling for which I believe many of you might be using Autodesk products already for that. And, obviously, compliance with the building codes.

So let's talk about a scenario where if we wanted to-- and I'm focusing particularly on a scenario which is connected to an office building and predicting smoke and fire behavior within that office building. Now, if I was able to create a virtual building that could be modeled with great accuracy and look at a what if scenario by using an example to investigate and preparing for a potential fire hazard, I can start evaluating that for different scenarios. Devise the best solution possible A, in terms of designing the building itself. Or B, coming up with a solution to mitigate this fire and smoke so that you can actually keep the occupant safe. And, obviously, building also which are designed to resist extreme situations.

The answer for this is using engineering simulations, you can tackle a lot of these questions and challenges that you might be posed against. This shows an example of a front desk of a Ansys UK building in Sheffield. This shows a smoke dispersion happening throughout the building as a scenario. And this is being done using computational fluid dynamics. So with that, you can see how the smoke is propagating through the office building. And based on that, you can either put mitigation measures in place or make sure that the exits are designed in such a way that it allows the occupants to escape.

So when we talk about simulation driven building design, and you talk about innovation, obviously there are a number of challenges that we mentioned before. Specifically, obviously, this is not a comprehensive list. You're thinking about selection and location of safety equipment. You're looking at behavior under extreme conditions, for example, under high wind load conditions. You're thinking about the stress and fatigue of parts components. You're thinking about the environmental impact. But also the environmental impact not in the sense of the building itself, but rather the materials that's being used for those. Which brings us to the question of sustainability. Obviously, stability, comfort, pedestrian, as well as occupants is paramount. And finally, as we are entering the era of smart everything wireless coverage, how does this buildings are designed for wireless coverage as well?

So simulation can help with solving a lot of these problems, be it external aerodynamics, ventilation, modeling of structure, designing safety equipment, seismic analysis, smoke propagation, data center cooling, and 6G wireless channel modeling. And we'll highlight some of these examples later on during our presentation.

So what does high fidelity physics really mean? And, essentially, I want to maybe take a few seconds over here just to explain that. Generally, when we talk about high fidelity physics, we essentially solving for general conservation equations, which are partial differential equations that you see on the screen, which represent the conservation of mass momentum and energy when it comes to fluid dynamics. This is a very famous Navier-Stokes' equations that's been developed over 200 years ago. And when we incorporate that into our solvers, essentially what we are doing is that we are discretizing the domain, which is a 3D volume, into smaller elements.

This could be done using different approaches. In CFD, we use an approach called finite volume method. And essentially what we are doing is that we are solving for those governing equations within those control volumes in a numerical fashion. By solving these equations, then we can actually predict the velocity, the pressure for fluid dynamics, displacement and stress and deformation for structural mechanics. And from that, you can extract quantities that you can actually use to predict what you care about specifically indoor air quality, pedestrian comfort, plume deployment in the smoke hazard case, and the building energy efficiency. So all in all, what we are trying to do is essentially bring in a full 3D geometry, which is then meshed and on which we are solving the physics numerically and from which you are essentially producing the solution.

So let's take a few examples of these in the context of computational fluid dynamics. So this one, specifically this section, I'll talk about a little bit on the wind engineering side. As you can imagine, wind engineering is quite important for buildings. Using Ansys software, engineers can understand the aerodynamics of a structure and the resulting pressure map on the surface. This will also define the structural load imposed by the wind with consideration for its static as well as dynamic loads, especially important for bridges and large buildings.

One case in point is two months ago I was in Dubai and I happened to visit Burj Khalifa. And it's an amazing feat of engineering which shows you that 2700ft building actually bends on its top section up to six feet because of wind gusts. Similar consideration is also done for, like I mentioned before, building [? solid ?] structures to make sure that they are compliant to the regulatory authorities codes.

By doing wind engineering, you can also find out the impact on pedestrian comfort and safety in the surrounding as well as address any environmental and safety issues. Case in point, the example that you see on the screen is of a dispersion of a fume that's happening within a city landscape up to about five kilometers. By being able to predict that using computational fluid dynamics depending on the headwinds, now you can predict how the dispersion of these gases and aerosols will take place. And then based on that, you can come up with a guidance on which section of the urban environment needs to be evacuated.

So talking about pedestrian comfort analysis, this is just an example that you're showing on the right hand side. You can see the movie. This was a movie which was a new building that was set up. But it needed to be analyzed with computational fluid dynamics because the surrounding buildings were actually higher than the actual building itself. And there was a concern that there is a potential to impact pedestrian comfort due to wind channeling. And you can see by doing CFD, you can actually start predicting regions of high velocity surrounding this building.

Similar to that, you can also evaluate other concerns. For example, how does the solar shade look like if I have a new building in place? So the video you see on the right hand side is essentially showing the sun shadow at different times during the day. And based on that, once you have done this analysis, you can find out whether or not the building needs to be modified and to ensure that it is not causing any kind of discomfort to the neighbors as well.

This is another example. Obviously, I talked about large structures. The scale of what you can do with computational fluid dynamics is tremendous. I mentioned about the situation of COVID, and this was an analysis that was done in collaboration with the Technology University of Eindhoven, where they modeled essentially a propagation of tiny COVID droplets, 1 millimeter thick, in environments such as a stadium. And the stadium consists of nearly 30,000 fans. And you can see that using CFD based on the different wind conditions, you can accurately predict the distribution of these droplets throughout the stadium. Now based on that, you can also make decisions on where do I need to put mitigation measures to make sure that this COVID particles can be either extracted out or doesn't actually get distributed to the masses.

So let's shift gears and talk a little bit more about the examples focused on ventilation and thermal comfort. As you can imagine, thermal comfort is a key challenge for building service engineers, particularly given the need to adopt lower energy approaches and making use of wherever possible passive technologies and considering a range of environmental conditions, be it summer solar gains, winter radiant losses, windy condition, et-cetera. And using CFD, you can predict different types of [? root ?] conditions. For example, air velocity, temperature, relative humidity, the impact of thermal radiation, and the distribution of toxins.

So obviously in this case, you need to account for the comfort criteria which means how does the indoor air quality look like for the occupants? Or if there is a high heat loss through the structure. This is also guided by ASHRAE Standard 55, which gives you the guideline for determining and achieving thermal comfort in occupied spaces. Obviously this is seen across the board for large buildings, large offices, commercial premises, public buildings.

And this example that you see on the right hand side was a particular scenario that was evaluated for a dual purpose stadium. In which for one season, it was used primarily for ice hockey. So you can imagine the ice is cold, so the HVAC system needs to account for that and make sure that while keeping the ice cold, the occupants also are not. Not encountering any kind of discomfort. On the other hand, for the rest of the season, it basically acts like a concert stadium. In this case, the design criteria was to ensure that the HVAC system is not noisy so that it doesn't impact what the concert goers are listening to. So there are multiple known strategies and concepts that can be developed with the help of computational fluid dynamics, including natural ventilation and minimizing energy consumption.

Another example is that of datacenter cooling. This is an example that you see on the right hand side of a datacenter room in Ansys, Lebanon, which shows our cluster. So essentially using CFD, you can virtually test and optimize the datacenter's cooling before we actually commission it. A lot of the non-obvious problems can be fixed very early doing. So you can model these airflows with high degree of confidence and also observe qualitatively the airflow patterns as well as quantitatively look at the different temperature profiles. And by doing so, you can evaluate different what if scenarios, plan for failure scenarios as well.

This example on the right hand side shows you the airflow patterns, which is colored by the temperatures in the datacenter. So you can see the flow coming in is in blue. But as it passes through the cluster, it picks up the heat. And this is done in red in color.

A couple of other examples. I already talked about the fire and smoke management piece. As you can imagine, regulations and safety concerns require that all the buildings are prepared for effective ventilation and detection systems in case of fire. Computational fluid dynamics can help you understand the spread of smoke and heat. Our solutions for these two areas have been used for a long period of time, and they can predict all stages from preliminary design through retrospective investigation of the fire modeling and smoke modeling as well. With this, you can essentially design efficient equipment for fire suppression as well. And I'll talk about one example towards the end of this section.

This shows another example of a smoke extraction in a major airport. Now, in case of a fire, typically, especially in an enclosed place with a lot of occupants, an emergency smoke exhaust system will essentially kick within one minute of the detection. This usually typically includes of emergency curtains which allows you to create zones for people to stay safe. Obviously, in this case, it's important to know the distribution of the spokes to the entire building in case the fire happens.

So the picture that you see at the bottom essentially is a fire scenario after 15 minutes with emergency curtains in use. And you can see the same counters on the right hand side, which shows you the distribution of the smoke. With this now, the designers can actually know how the smoke spreads across the entire building and use position control curtains wherever they're needed the most. In many cases, the building contains buildings such as atriums, public buildings, have to have a smoke management strategy.

So I talked about fire suppression. Just like the way you're able to predict the smoke and fire, you can also design fire suppression system. And many critical areas have built in fire suppression devices. The example that you see at the bottom is of a chamber of a ship with machinery. And by being able to even predict the droplet size that is needed to be modeled in the complex geometry and simulated, you can start optimizing the location and the effectiveness of the fire suppression system.

So what you see on the screen over here at the bottom essentially is how this fire suppression system is deploying these droplets for suppressing the fire in various locations. And you can essentially redesign that for optimal behavior.

I talked a lot about the examples on fluid dynamics. Let me talk a little bit more about structural design and stability. As you know, we talked about the loading conditions that's seen on many buildings and structures. So investigation of the construction components and the whole buildings can be done using Ansys mechanical software, which is our structural analysis software for predicting the response during both normal as well as extreme loading conditions.

Now this allows you to do a range of types of problems in civil engineering. This includes linear analysis, non-linear, static, and dynamic analysis as well, and make sure that they're conforming to the stringent requirements for AEC. In many cases, you have wind induced deformations. There are cases on where you have to predict the analysis based on earthquake conditions. There could be cases where there is sand loading on the building. How do you account for that? So this range of solutions can be used for high rise buildings, bridges, dams, theaters, stadiums, et cetera.

And this is an example of a floating staircase design for which we did structural analysis. Obviously, this was a unique design in which a floating staircase didn't have a support in one portion of the staircase itself. But the questions that we are trying to answer really is would the material fatigue of this staircase endanger the inhabitant after a decade or so because these buildings are going to be used for many, many, many years. Can I use a cheaper material without compromising any kind of safety?

With simulation, you can predict the behavior of this staircase, for example, on its stress, strain, and displacement components. So the example that you see on the right hand side shows two particular designs where you are able to model that for structural response. And you can find that there are regions within those floating staircases which are high stress and need to be accounted for before you deploy the building.

Another case where the structural analysis is essentially useful is buildings and other structures where deck and weight of the structure needs to be accounted for using standard design rules. Wind loading, as you know, can be pretty significant, especially for bridges.

One of the classic examples of a bridge failure because of wind gusts is the Tacoma Narrows bridge. This was a bridge that was built across a narrow channel near Tacoma, Washington, back in 1940. But after two years of construction, when it was opened up, it started encountering buckling and rolling. And on one particular day, after four months, it encountered 40 mph winds, which led to the failure of the bridge itself.

And this is a classic case of study where there was potential reasons to believe that the bridge would have encountered structural-- essentially a natural response to the wind gusts, which led to its failure. Or an impact such as aeroelastic flutter, which is a response of the structure to the external wind conditions. So when you're designing these buildings and structures and these bridges, you need to account for the natural frequencies of the bridge itself to make sure that its away from the potential wind response as well that you see that the bridge would normally encounter.

In addition to that, there are other analysis that can be done, for example, seismic analysis for both components as well as large structures like stadiums. When it comes to stadium design, sometimes you have to account for rhythmic mass movements that might happen when the stadium is being used for something like soccer or a concert. And similar conditions can also be explored from both implicit dynamics as explicit dynamics, such as blast, projectile impact, et cetera.

Now, I know that I walked through a lot of examples on both for free dynamics and structural mechanics. And one of the questions that gets asked is when should we really do the analysis? Obviously, analysis can be done later on to correct any issues. However, it is far more expensive than doing this analysis early on to avoid the mistakes itself.

Now, Ansys has multiple technologies to address the various aspects of fluid, thermal, structural, optical and image analysis. However, someone might think that the simulation itself that I've shown so far looks very cumbersome and I should probably leave it to the experts. And the other question that you might have is, if I'm working in Revit, how do I bring my Revit models into Ansys solution?

So we have been very aware of this problem. And Ansys for the past few years has been working on a solution which enables upfront simulation with speed and accuracy. This basically combines interactive modeling and multiple simulation capabilities in a solution called Ansys Discovery. And can answer your critical design questions early in the design process.

So let me show you a demo of how that really works. And this is a short video showing two different examples where we are able to bring in the Revit geometry. As you can see, the first one is that of a pharmacy for which we will analyze the external flow around the building. And once you bring the geometry in, it basically creates this faceted bodies that you see on the screen.

Once I'm done with that, I need to create a fluid volume, which is essentially inverse volume to the building itself. For that, I create a rectangle. And then using direct modeling, I'm able to extract it out, creating this volume. Once I'm done with that, I'm going to analyze this for different types of wind conditions.

First one, I'm going to specify facing the building a direct flow of 10 miles per hour. And then for the rest of the faces, I'm going to specify an outlet, which basically is a normal pressure allowing the flow to go out. So the wind is only coming from one direction.

Once I'm done with that, I'll basically rename my geometry calling it the flow volume, which is different from the Revit bodies. And then I'm going to do an important step, which is setting that as of [? cutter ?] body. So once you do that, you're extracting the fluid volume to represent the buildings inside.

So once you're done with it, you click on one button within Discovery. And you can see that Discovery is able to use this GPU solver to rapidly calculate the flow around the building. And I can visualize the flow from different angles. I can take a vertical cut. I can take a horizontal cut. You can see that the suppression of the flow. There is a flow going near the entrance of the pharmacy. I can take a vertical section to see how the flow distribution looks like.

I don't want to stop there though, right. I also want to make sure that I'm able to pull some quantitative data out of this as well. So the first thing I do, I create a monitor point near the entrance of the building because I want to make sure that when the wind is flowing around the building, it doesn't cause any kind of discomfort for the person entering the building.

There is a smoke break area, I guess, where people might be standing. I'm going to set another monitor point over there. And the third place I'm going to set a monitor point on is behind the building next to an open garage. So what I'm going to do over there is going to specify a phase instead of a monitor point. And now by setting up the monitors, I'm going to select each one of them. And I'm going to specify the quantity that I want to measure over here.

So for the first monitor point, I'm going to select velocity. Which is the average value for the second one. I'm going to also specify the average velocity. And I'm going to rename that as a break area. The first monitor point as the front entrance. And the third place, I'm going to actually monitor the volume flow coming through that phase. And I'm going to call that the garage entrance.

So after having done that, now I already have this values for one flow. I want to also change the flow direction a little bit now. I'm going to specify a 45 degree angle to the flow by specifying the components. Now I also have the x direction as well as the y direction. And as I click on the button, you can rapidly see that now the flow has now changed, now it's coming at an angle to the front facing of the building. So I can, again, look at the streamlines of the flow. And also visualize this using particles. Now, by setting up the size of the particle and setting up the range for that, I can also visualize where the high speed flow is happening and where is the low speed flow is happening. As you can see in the front of the building now, the flow speed has not gone up.

Let me evaluate a third criteria in this case where I'm going to switch the flow from one phase to the side of the building. I'm going to do the same thing. I'm going to remove the inlet. I'm going to select that phase. Specify that to be a new inlet flow and specify that to be 10 miles per hour again. I'm going to change that from an outlet to an inlet. And then the rest of the phases, I'm going to do the same thing as I did before, specify all of them as outlet.

Now, when I click on the Solve button again, the solution now you'll see will start evolving, showing the flow from the side.

So you can see from the top that there is a massive recirculation happening because of the corners of the building. And then now you can see flow is actually going into through the garage. And there's a recirculation zone in the break area as well. And if you look at the different monitor points, you'll start seeing that now the garage flow through that particular phase has increased significantly.

And, similarly, if you look at the front entrance, you can see that the flow rate through that particular point has also gone up. So I'm going to hide this. So we'll try to remediate that by essentially using our direct modeling feature where I'm going to increase the size of the garage, both in height as well as in width by putting those faces.

So just notional depiction of the concept over here. I'm going to change that size average rate a little bit here by pulling those faces. And I'm going to pull the side face as well. And make some adjustments to the original wall over there. And because I changed mesh size of my garage door, I'm going to also increase the phase that I'm measuring the flow rate at.

So I'm going to redo the analysis now. Same way, you don't expect a major change in the flow around the building itself, but we should see at least some change in the flow that is coming through the garage door. So as you can see, once the flow converges you will see the new monitor point. This is still going through the iterations right now. And as soon as it's done, you will see that the monitor point number five now shows you a new value, which shows you a different flow rate that's coming through the face.

Let's shift gears and use a different example. We talked about the external flow. I'm going to look at internal flow in essentially a residential building, which is essentially a house. And we want to be able to predict how the HVAC system needs to be designed for this particular house.

So this problem was preset a little bit earlier. Essentially, we created already a flow volume like I showed you before. Now, as you can see, this is a residential hall home with the flow volume. And the faces that you see on the top are essentially the vents through which the HVAC system is delivering air into the house. I have also a couple of faces where there is flow coming out of the house as well. I'm going to set them as outflow.

And then I'm going to show the body again. And then I'm going to solve this where I set a flow for one feed per second coming through all of those vents. As you can see, there is a nice distribution of all these streamlines in the complete home. But you can see that by toggling a little bit on the particle sizes and adjusting the range of that, now I can start to see the areas where the flow rate is significantly low. So you can see there is the space in the living room, in the front and the back, that has very low air coming in.

So I'm going to now make some adjustments. I'll select one of the faces and duplicate it and place it in the middle of the living room now. I'm going to set the flow velocity again to one feet per second. Same distribution. And I'm going to redo the calculation again with this new vent in place.

And once you run the analysis, you can see the airflow distribution throughout the complete house. Now you can see there's a little bit of a better airflow in the center portion of the living room, but probably not as high as you want. So you can see high velocity distribution in that region.

So what we'll do now, we'll make further adjustments by moving this vent to the back of the living room and then duplicating another vent in the front of the living room. So essentially replicating the same process as we showed before. Specify a new value there for, again, for one feet per second. And hopefully with this adjustment, now I can essentially get a better flow rate throughout the entire house.

I hit Solve. And it should solve solving rapidly. And you can see all of this is being done real time. And with the new adjustments, I can see now I have a better flow coming through these vents in the living room. And it can also measure like change the count to the maximum range for that to see how the airflow is getting distributed.

So that's an example of how real time simulation can be used to predict both HVAC systems as well as external air flows. With that, I'll pass it on to Juliano to talk through some examples of wireless channel modeling.

JULIANO MOLOGNI: Yeah, thanks, Renni. Those are very good examples. And I'm going to try to replicate that using electromagnetic simulation. So on the next slide what you're going to see is that we live in a smart world. Which means that we have antennas everywhere. So, Reni, if you could please change to the next slide?

We're going to see an animation where you see antennas on a car talking to each other. You have cell phones, base stations, they could be 5G, they could be LTE. And you have your cell phone where you have a handful of antennas, right. So in a car, we usually have dozens of antennas. Airplanes, we can have up to 100 antennas. So we live in a smart world, right.

And one thing that is very important is where you're going to place those base stations. Where you're going to place your Wi-Fi router. I mean, if you're living in a small house, that's OK, right? But if you're living in a commercial building you want to have a very good coverage. So the same heat maps or the fluid maps that Reni was showing, we can do pretty much the same thing with the electromagnetics.

In the next slide. What we're going to see is a new product that is called Ansys Perceive EM. So Ansys Perceive EM is a GPU-based electromagnetic solver that can provide you real-time or near real-time information on the communication channels. So the technology that we use here is based on what we call shooting bouncing rays, which I'm not going to go into much details. But it basically, you have an antenna, which is designed with other tools like Ansys HFSS which is a true full wave solver. And we take that antenna and then we should rays everywhere.

So when we shoot rays, we compute the reflections and the fractions of the electromagnetic wave. So Perceive EM can be embedded into any environment that you already have. So imagine that you have a database of buildings or a city like what you're seeing here. You can just basically place antennas, and we're going to provide you all the communication outputs, including the channels, modulation, all the IQ Information. And we can also provide radar imaging. And that's what we have in here.

So on the next slide, we're going to see examples. And let's get first started with a digital twin of a stadium using synthetic aperture radar image. So what you're seeing here is a 3D digital twin model of a stadium, right? Once you have that, you don't even need to build your stadium, right? You already have the CAD, right? Like in Autodesk. We can take that information and you can have an airplane and you can take that SAR imaging SAR stands for synthetic aperture radar, right. And you can get that information.

So here on the right hand side, you see the synthetic data that it was generated by Perceive EM. Click Next please, Reni. And you should see the real SAR image from the Capela space. And you can see that the real synthetic aperture radar image matches extremely well the Perceive EM data. What are the differences here?

The real radar data, requires you a real radar. It requires you a real stadium, right? But with Perceive EM you can get exactly the same information but with a digital twin. You don't have to have the stadium, right. In this particular case, it's a stadium. But this technology has been widely used for automotive radar, for example. You need to feed information to radars so they can understand it's a person crossing the street. It is a dog. If it's someone's falling down. And doing this in the real world takes a long time. You can do this by using electromagnetic simulation like what we're seeing here.

On the next slide, since we're talking about stadiums, one of the things that we can do is to place Wi-Fi routers and 5G repeaters so you can have a very good coverage. In this particular example in here, we have an antenna ray-- a millimeter wave, 5G antenna ray where you can steer the beam. The things that you're seeing on the left hand side is the radiation pattern. We can electronically control where we're going to focus the electromagnetic energy. And we're going to have a good or bad coverage, right.

And the plot that we're seeing of this stadium, the red shows where we're going to have very good coverage. And it goes down to the blue where there is no coverage. And this is from only one antenna array, of course. We want to place multiple antenna arrays to have a very good coverage. But you don't want to place too much of them, right. Because it's not only expensive, but they can interfere with other electronics.

On the next slide, what we're going to see now is one of our latest partnership with NVIDIA. So NVIDIA, we use all the NVIDIA GPUs to perform all those electromagnetic simulations in almost near real-time, but we also can use the Omniverse as a database to run those simulations. So if you click Next, Reni, please, what you're going to see is actually scenarios from the Omniverse and real data from Perceive EM.

So on the right hand side, what you're seeing is an autonomous robot inside of an industrial complex. And what you're seeing is actually near fields, electromagnetic near fields, because it needs to know where they have to go, right. It needs to dodge some of objects that you have in the path. And it needs to understand the environment around it.

On the left hand side, what you're seeing in blue is what we call the range doppler map. It's how the electromagnetic radar sees the environment. What you're seeing right now is a car that is driving on a street like this, and you have a radar on the front of the car. Now you see there's a person crossing the street and you can see that on the electromagnetic range doppler map. And those things that you're seeing here, the visuals are generated by the NVIDIA Omniverse. But we're taking that information and we're running full physics based simulation to generate communication response and also radar sensing imaging.

On the next slide, what we're going to see is a more complex scenario inside the NVIDIA Omniverse. What we're showing before was radar. Yes, please. Thank you. And what you're seeing here is actually the shooting and bouncing rays. And we have some of the user equipments and one RU, the receiver unit, right.

What we're training here, we're predicting the channel response. So have a bunch of cell phones and you have a receiver unit. And we're simulating in this entire environment, the reception in the frequency domain and in also in time domain.

On the next slide, what we're going to try to show is a more complex simulation. You can hit Next again, please.

So this is more dynamic. It's more what you're seeing in the real world. But it shows the power that we have in here to simulate those really complex scenarios.

So what you're seeing is a map. You have four blocks in red. There's a path where we're going to have a drone with one radar and also some communications. In blue, what you're seeing is actually the path of a few vehicles. So we have some cars with radars and also some radios. In green, you have some people, pedestrians, they're walking around and you have some crowded areas. So we're solving this with GPU and Perceive EM.

On the next slide, you're going to see two cameras. You can place cameras anywhere. So one is following a person on the left side walking. And you can see the rate of response of that person with all of these cars. On the right side, you see the drone. So the drone is flying following that red path. And you see on the top, the radar response, the doppler range map. And also down on the bottom, some of those squares, those are the multi-channel frequency domain response from the base station to the quadcopter.

So we can simulate from any database. In this case, we got the geometry courtesy from Aerometrex. But we got that from NVIDIA. If you have already an environment where you have lots of geometries, let's say for camera or lighter, we can take all of that and already plug it into our Perceieve EM solution and provide all of that kind of information.

The next slide will show you how the electromagnetic wave propagates. So imagine that you have a base station. This is exactly the same map one. It's an isometric view on the left side. And on the right side is on the top. If you click Next, Reni, what you're going to see is a heat map of an electromagnetic wave energy at 30 gigahertz, millimeter wave, 5G millimeter wave, and how that reflects. And you have refraction in all of the geometries that you have in your model. So very similar to what Reni was showing with the fluid flow, we can do the same thing with the electromagnetic waves.

And on the next slide, I think what I'm going to show you is that we are talking more about city level simulations building, right? But we do have the technology that helps design from chips. So all the chips that you have in your computer, in the cell phones, they're designed using Ansys technology. The electronics, the print circuit boards from your phone, your computer, again, your TV, again, they were simulated and designed using HFCs.

We saw a lot of simulations here, like city level or building level size of simulations. But you can go even further, right. We have missions-- like if you have satellites in orbit around the Earth, we can take that information and compute the communication from a satellite to base station.

So what I like to say is that Ansys is the leader in Simulation from silicon to space across all the physics. Here we were highlighting some of the computational fluid dynamics, mechanical, and also electromagnetics. But Ansys, we have a much broader portfolio. So with that, I'll turn it over back to you, Reni, for final conclusions.