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Change the World with Autodesk Fusion 360: Become a CFD Engineer with Generative Fluids

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说明

Have you ever thought about flow path optimization? Do you have great ideas that could change the world, but no fluid mechanics knowledge, time, or expensive CFD tools? All you need is Generative Fluids in Autodesk Fusion 360 software! We'll present a set of case studies in this session. The first ones will explain the basic aspects of flow path optimization. We'll discuss pressure drop, its reasons, and minimalization solutions in an easy-to-understand way. Then, we'll discuss more-complex designs in order to show how to use Fusion 360 capabilities and change inner flow paths into outer shells that are fully manufacturable parts. And we'll bring a few of our 3D-printed designs so you'll have a chance to handle them! By the very end, you'll understand the full potential of Generative Fluids optimization possibilities. This is the beginning of your flow optimization journey!

主要学习内容

  • Learn how to use Generative Fluids.
  • Learn how to optimize the flow path for various cases.
  • Learn about designing manufacturable parts.
  • Learn about the basis of the flow behavior.

讲师

  • Maciej Jaskiernik-Detka
    Maciej Jaskiernik-Detka is a Senior Software Quality Assurance Engineer in the Generative Design and Simulation group at Autodesk. He joined the company 2 years ago to improve quality and introduce new features in fluids simulation products. He worked several years in automotive industry as CFD Simulation Engineer, where he was responsible of creating methodologies for advanced thermal simulations. He enjoys converting complex physics to customer friendly products. Maciej studied Energy Engineering at AGH University of Science and Technology in Krakow.
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Transcript

MACIEJ JASKIERNIK-DETKA: Hello, everyone. My name is Maciej Jaskiernik-Detka. I am a senior software quality assurance engineer at Autodesk. And today, I'll show you my presentation, which I named "Change the World with Autodesk Fusion 360-- Become a CFD Engineer with Generative Fluids."

Today, I will shortly present you some fluid path features, such as pressure drop reduction, flow balance, obstacle omission. Later on, I will show you applications in manufacturing. Especially, I will focus on how to create manufacturable parts based on your design. And in the end, I will show you how to find fluid path in Fusion 360.

I need to make one statement. Fluid path and generative fluid is the name of same sub-model in Fusion 360. So I will use both names during the presentation.

Let's start with pressure drop reduction. The slides I'm going to show you were prepared for the students, which were visiting our office in Krakow about one year ago. And I was asked to show them what is [INAUDIBLE] the fluid in general and what does a CFD engineer do for a living.

So the easiest problem for CFD engineer to solve is connecting two pipes into one. The goal is to achieve the lowest possible pressure drop in the connector. And in a minute, I'm going to tell you why.

In big companies where I used to work, first, this problem is handled by design engineer. So design engineer is always focused on producing cheap and easy-to-manufacture solutions, like the one in the left picture. So the idea is that somebody is going to cut the pipes, weld them again, and create the connector, as in the picture.

Later on, design engineer exports this idea as a CAD data and provide it to CFD engineer, like I used to be. Now it's time for CFD simulation, based on which I will calculate the pressure drop.

And now imagine that this connector is a part of the bigger system-- for example, internal combustion engine cooling system. So if the pressure drop in such system is high, it means we need lots of power to get it running. Therefore, we increase the fuel consumption in the internal combustion engine. And this is definitely not good either for the user nor for the environment. And usually, it comes out from the simulation that the pressure drop is too high.

So we have two possibilities-- either to increase the water pump, and also the fuel consumption, or to optimize this pump, maybe change the manufacturing solution. But typically, option number two is chosen.

So now I need to prepare some tips because I'm not able to prepare a new design on my own because I do not have almost no design skills. Therefore, I'm only preparing some tips, as you can see in the picture on the right side. I'm drawing some lines in PowerPoint. And I'm providing it to the design engineer.

But later on, he or she comes to my desk and asks, but what's the radius of this line? And I'm saying, I have no idea. I just draw it in PowerPoint. You need to figure it out. And that's problematic because this process that takes lots of time. And therefore, it's quite expensive. But fortunately, we have generative fluids.

In generative fluids, only need-- only thing that you need to do is prepare a simple shape as a starting one for the optimization. So you need a separate body for inlet or outlet and, as I said, a starting shape.

The aim of this optimization is to minimize the pressure drop. So first, a CFD simulation is run, based on which a sensitivity field is calculated. Sensitivity field indicates which part of this model should be enlarged or where it should be cut.

So as you can see in this slide, iteration by iteration, you get smoother and smoother shape. And therefore, you decrease the pressure drop. The result is shape as presented on the screen right now.

And during the presentation, which I showed to the students, I told them that for sure, this one can be manufactured, either used as a core in the casting or printed using 3D printers. The truth is I haven't-- I wasn't sure about that. Luckily, a couple of weeks later, I took part in a 3D printing training. And as we have a 3D printer in our office, I decided that I need to manufacture it to prove that I was right.

So I started with preparing the outer shape because as you imagine, generative fluid is optimizing the flow path-- so the inner shape. Therefore, you need to create the outer shell that you can manufacture later on. So I started doing this. But later I thought, if this is a connector, you need an interface, for example, to clamp the hose on it. Therefore, I extended inlet and outlet a little bit.

Next, I wanted to get rid of the support in the printing process. So support is the scaffolding that needs to be introduced during the printing process to support the structure. And I introduced a leather shape in the middle to get rid of this support, and also to stiffen it a little bit because I had no idea if 2 millimeters of the wall thickness, which I assumed, is enough.

In the end, I thought, but what about sealing? When you clamp a hose on the connector, you want the interface to be perfectly sealed. Therefore, I introduced the ribs on the inlet and outlet interface, as presented in right picture. And I was ready to print it. And I was really proud of this process because it was the first thing that was manufactured by me and designed by generative fluids.

And at this point I thought, I need to go to the AU to show you this. I thought that if manufacturing is so easy, I will have lots of ideas. But I need to think of something more complex. But I was wrong at that point.

Nevertheless, I started thinking about the flow balance. So flow balance is a generative fluid feature which adapts the geometry to distribute the flow evenly between all outlets. So if we have a structure with one inlet and two outlets, the aim of optimization will be not only to minimize the pressure drop, but also to provide the same amount of the fluid to each outlet.

For demonstration purpose, I prepared a model with one inlet and six outlet. And honestly speaking, it was spring. And I was cleaning my garden. And I thought, a perfect design would be a garden sprayer which distributes the flow, water flow, from one garden hose to six outlets or evenly around the sprayer to my plants. And I started preparing it.

But as I told you, my manufacturing experience was almost zero. I cannot say it was zero because I created connector previously. But during the first run, I haven't thought about manufacturing restrictions at all. And it was a big mistake.

So this process of preparing the final design took me two and a half months. And I will share with you with my thoughts that I had during this process in the second part of the presentation.

Now, the important thing is that I was able to print my final design in one piece. And what's more, I run a detailed CFD simulation using Autodesk CFD. And that simulation showed that the flow imbalance is less than 5%. So for me, it was perfect.

Final design was a mix of manufacturing restrictions, flow rate uniformity, and pressure drop reduction. As you can see in the picture, I was able to print it and test it in my garden. And I was really happy about the fact that I made it. And I think it is really useful design, at least for me. But the question is, can you think of engineering application of this attribute?

So as my background is automotive, I can-- I prepared some examples for you. Let's imagine that we have a battery pack for the electric vehicle. So in this battery pack, you have cells, which are represented in the picture with red cylinders, and cooling channel, which is represented by yellow body.

So every time you charge the battery or discharge it, the cells warm up. So you need to cool them down. And typically, in such a system, you will have one coolant pump. Therefore, you need to distribute the flow from one inlet to all the cells. So it means that you need a distribution manifold.

I prepared such a manifold. We have one inlet and six outlet. So green bodies are keeping the boundary conditions. And they will be not modified during the simulation. Yellow body is a starting shape, which I prepared using automated modeling. And if you don't know this feature, I really recommend you to get familiar with it because it speeds up the design process a lot.

The outcome of this optimization was the geometry that you can now see in the screen. So please take a look at the outlets. The flow velocity close to the outlet is almost 2 meters per second in all areas. Therefore, we might think or assume that the flow balance is properly distributed or the flow is properly distributed.

Nevertheless, to prove this information, I, again, run a detailed CFD simulation using the distribution manifold and cooling channel. And again, this simulation showed that on the outlets which are now on the left side of the geometry, the flow imbalance is around 5%. Of course, the flow in the cooling channel can be still improved. But I think it's really good for the first shot.

Another attribute of generative fluid is obstacle for omission. To visualize this possibility, I prepared the sprayer with one inlet and one outlet. Honestly speaking, I was not sure if the structure can handle the water pressure inside. So I prepared a stiffening structure between-- which will be later used to connect the outer shell of the top-- the bottom part. It is represented at the-- with the right shape. This body is defined as the obstacle. So optimization algorithm will omit it during the optimization process.

And as you can see, this obstacle is introduced in a way that takes some of my flow path. Therefore, it's decreasing the cross-section area and, for sure, increasing the pressure drop. Fortunately, generative fluid can provide a perfect shape which perfectly omits the obstacle. It's very smooth. So we can expect that pressure drop was reduced. But we need to prove it. Therefore, I, again, run a CFD simulation and printed this structure.

So simulation showed that pressure drop assuming 1 meter per second before the optimization was 14.5 kilopascal. But after optimization, it was reduced to 6.35 kilopascal, whereas a test in the field showed that spray distance has increased from 140 to 210 centimeters.

The reason why the pressure drop was reduced over 2 times and water spray distance has not increased by 2 times is the fact that the nozzle inclination is 30 degrees. Therefore, it means that the water goes higher than further. And I focus only on the distance from the sprayer.

Again, we need to think, is it really useful? So back in the days when I was responsible of preparing cooling systems in the internal combustion engines, I always had a problem with something called thermal management component.

So this is the model that distributes the flow, for example, from engine water jacket, charger air cooler, or engine air cooler to the radiator, to the bypass of the radiator, because you know that, for example, during winter, when you start up your engine, you don't-- and the coolant is almost freezing, you don't want to cool it down. You want to warm it up as soon as possible. That's why you need this bypass.

And of course, you also want to warm up air in the cabin. Therefore, you need another airflow to the passenger compartment heater. But if you ever try to change a bulb of-- in your car, you know that the packaging under the hood is really tight. So most probably, we'll need to protect some space for other engine systems. And this protected space is represented by red cylinder in the picture, whereas the shaded area is our initial or starting shape, again, prepared by the automated modeling.

Later on, we need to think of how to connect this model to the engine. Most probably, it will be bolted. So we need to protect some space for the bolts, but also for the tools in order to later on access the bolts because you need to screw and unscrew these bolts. And this space is represented by blue cylinders in the picture.

Based on these assumptions, I prepared a generative fluid model. So we have green bodies, preserves, which are, again, keeping monitoring conditions and red bodies, obstacles. Yellow body is a starting shape. And of course, I could redesign it right now to omit the bolts. But I wanted to show you that generative fluid can handle a complex problem.

So flow velocity on the inlets is assumed or-- to be 1 meter per second, whereas I needed to play a little bit with the pressures on the outlet to avoid throttling. The result of the optimization process is geometry that avoids all obstacles. And what's more, if you take a look, we cannot see any rough edges. That means that pressure drop is quite low.

Of course, to create this component, lots of work needs to be done. We need to prepare interfaces for the bolts, design the outer shell, lots of things. But designer who is not familiar with the CFD can use this shape as the internal flow structure. And if further optimization is needed, he can-- he or she can always use preserves and obstacles for detailing and target volume to change the general dimension. I will elaborate about the target volume a little bit more in a couple of minutes.

Let's sum up. Fluid path can smooth your design and reduce the pressure drop. It can provide you even flow distribution and omit all obstacles that-- occurring on your system. So I think it is really useful thing.

Now let's focus on applications in manufacturing. As I told you, my story or my journey with a content sprayer was-- took about two and a half months. And one of the most important things I learned that-- is that starting shape is really important because [INAUDIBLE] the fluid can only optimize your idea. It cannot provide a new design.

In the bottom on the right, you can see geometries that were-- the alternative geometries that were prepared by automated modeling. But I decided to use the one on the left side. And if that was a good decision we'll see in the next slides.

Another important part is defining a target volume. You need to provide it as a percentage of the starting shape. So optimization algorithm needs to know whether to increase your starting shape or decrease it, and how much.

It's quite obvious that in the beginning of the process, you have no idea what should be the final volume. Therefore, I recommend to run multiple simulations and choose the one that fits best to your requirements. In my case, I chose the one with 30% of the target volume.

When you choose your final design and you want to generate a new model, click on the "Design from Outcome," wait a while, and when it's ready, click Open Design. This operation will provide you a new model you can start working on. And as my sprayer was too complex to print it in one part, I decided to split it to the base and the top part. And I prepared a-- slightly different workflows because a base is very simple shape. Therefore, all you need to do is start from creating the surface with the offset.

Think of the offset as the final part wall thickness you want to achieve. So in my case, I wanted to have a 3-millimeter wall thickness. Therefore, I defined the distance offset as 3 millimeters.

Next, you need to thicken your surface to the inside to perfectly represent the shape that you use during the optimization process. Therefore, I used thickness value of minus 3 millimeters.

Now it's time for detailing. As I wanted to use two parts and connect them later on, I needed the interface for sealing. I also introduced some positioning pins. And, of course, I needed a connector between my garden hose and the sprayer.

Now I can start working on the top part, which is a bit more complex. So I was not able to create the surface offset with distance of 3 millimeters because such surface would have self-intersections. Therefore, you need to start with creating the surface with offset of 0 millimeters, just representing exactly the shape you obtained.

Next, you need to mesh it. Use Tessellate tool. And at this point, you can use default meshing options because it's not really important. But as you can see in the picture, there were several surface meshes created for each surface. So to simplify the workflow, you need to combine them into one using Combine Face Groups tool.

When it's done, you can start repairing the surface. And if you use Repair tool and choose the repair type to Rebuild and rebuild type to Accurate, you will be able to provide the offset and create a volume mesh. Again, think of this offset as the final part's wall thickness. I've used 3-millimeter distance.

Right now, you need to convert mesh into a solid body. This operation is really time-consuming. One thing you need to consider is reducing number of elements to speed up. Of course, if you reduce the number of elements, you will lose some quality of the surface representation. Therefore, you need to think on your own about doing this step. Nevertheless, if your-- when your solid body is ready, the last thing you need to do is cut the inner flow path inside it. So using the optimized geometry, we can prepare shape for the fluid flow.

Remember that offset is providing some volume not only to the side, but also to the top and bottom. And in my case, I needed to cut the bottom 3 millimeters to, again, open the connection between top and bottom. And nozzles were prepared in the separate steps using the simplified workflow because these are really simple shapes.

Finally, I obtained two geometries which were ready to print. Base was not a problem. But top structure contains quite a lot of overhangs. Therefore, lots of scaffolding was needed during the printing process. And as part of this, supports were not able or impossible to be removed.

Later on, mechanically, I needed to use a PVA, which is water-dissolvable material. So you can print it, put it into water, and get rid of the support. Perfect. But unfortunately, this material is quite unstable. And as you can see in the picture, the printout quality was quite poor. Therefore, I needed to simplify my idea and start printing process again.

If you consider 3D printing, you need to imagine that one layer is printed over the previous one. So it's-- the easiest shapes to create are pyramids or towers because it's always easier to put a smaller object on the bigger one than the opposite. And that was also my idea.

So my initial shape during the second run were six towers connected with a circular connector in the bottom. And the outcome of this optimization process is visible in the middle. Using the workflow I showed, I prepared the outer shape. And I was ready to print.

As you can see in the left top pictures, the printout quality is good. But I was really unsecure about how to connect these two parts. And my colleague advised me to use zip laces because they are indestructible. But unfortunately, they just don't look good. Nevertheless, I used them. And as you can see in the movie, I was able to test my garden sprayer for the first time.

But I had a couple of problems. First, I didn't like the way it looked. The zip laces are indestructible. But I didn't like this idea.

Second, there was a sealing issue between these two parts. And the last problem is spraying distance. As you might see in the movie, the distance, which was provided with water, is-- the circle is about 30, maybe 40 centimeters of the radius. So it's quite small. Therefore, I needed to prepare another design.

I was thinking about pyramids and towers. And I came up with an idea to change the circular connector to be a bit more similar to the pyramid. So it has a triangular cross-section. And it's easy to print. But we need to optimize it. And also, I used a rectangular inlet. And this was very strange idea for me because I had never seen rectangular pipes. So I thought, maybe I'll be first in the world to use such pipes. We'll see.

But as you may expect, there is another issue that the connector, the silver one with the cross-section on-- of the triangle, will be not utilized by the flow. Therefore, generative fluids during the optimization process will try to remove it.

What's the solution? During the optimization process, you are provided with several designs, starting from the initial one. And in the end, you get the final, most-- or the fully optimized one. But there are several in the middle.

So the solution is to find one which still contains manufacturing features and is properly balanced regarding the fluid flow and is smoothened to reduce the pressure drop. And in my example. I choose the outcome number 10, which is now visible in the middle of the screen.

So I'm ready to prepare the outer shape. Of course, I need a custom connector between the hose and the garden sprayer. But it was not a big problem, as I was really happy because I fulfilled all my requirements. And I was ready to print.

I was very nervous during this process because it was the most difficult or most complex part that I ever manufactured. And I couldn't look to the inside to check the print quality. Nevertheless, the printing process was done with no issues. I have no sealing problem because it's one part. And I obtained even flow distribution, which I knew from previously run CFD simulation.

As you can see in the pictures, I really love this idea. And I use it in my garden. So it's quite useful for me. But let's focus on the lessons I learned.

So at first, think of the manufacturing restrictions while you are preparing your initial shape. This is very important. As you might see in my process, it defines either you are able to manufacture your part in the end or not. And that's why we need to focus on the second thing, which is that-- the fact that it's better to choose a manufacturable design than fully optimized one.

So it is true that done is better than perfect. What's more? Try getting inspiration from the forms around you. And usually-- not always, but usually-- the simplest ones are the best. And the most important thing-- if you fail, don't give up. But improve your design and try again.

So where is fluid path in Fusion 360? Whenever you open up Fusion 360, you start in a design workspace. So as I told you in the very beginning, you need a separate body for inlet or the outlet and a single shape for the starting shape. When it's ready, click on the Design button to change the workspace, and choose Generative Design Workspace. When the pop-up appears, check the Fluid Path and click on Create Study. You are now ready to set up your model.

At least one inlet and one outlet is needed to perform a simulation. Starting shape is not a must. But I really recommend to use it. Keep in mind that by default, water is your fluid medium. But you can change it to the [INAUDIBLE] or select Custom Fluid if you want.

So now you know where to find fluid path. You know that it can provide you a smooth design, reduce the pressure drop, and the amount of the energy you need to use to put the flow through your parts. It can provide you a proper flow distribution if multiple outflows are in your system. And it can avoid all the obstacles that you define.

I think that you are also ready to prepare manufacturable parts based on your ideas. Therefore, I think you are ready to change the world. But if you consider that my examples are too easy or not realistic, the best solution is try it on your own now. And please share your feedback using Fusion 360 Feedback Hub.

Thank you very much. I hope you enjoyed, and have a good day.

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我们通过 Salesforce Live Agent 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Salesforce Live Agent 隐私政策
Wistia
我们通过 Wistia 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Wistia 隐私政策
Tealium
我们通过 Tealium 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Tealium 隐私政策
Upsellit
我们通过 Upsellit 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Upsellit 隐私政策
CJ Affiliates
我们通过 CJ Affiliates 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. CJ Affiliates 隐私政策
Commission Factory
我们通过 Commission Factory 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Commission Factory 隐私政策
Google Analytics (Strictly Necessary)
我们通过 Google Analytics (Strictly Necessary) 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Google Analytics (Strictly Necessary) 隐私政策
Typepad Stats
我们通过 Typepad Stats 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Typepad Stats 隐私政策
Geo Targetly
我们使用 Geo Targetly 将网站访问者引导至最合适的网页并/或根据他们的位置提供量身定制的内容。 Geo Targetly 使用网站访问者的 IP 地址确定访问者设备的大致位置。 这有助于确保访问者以其(最有可能的)本地语言浏览内容。Geo Targetly 隐私政策
SpeedCurve
我们使用 SpeedCurve 来监控和衡量您的网站体验的性能,具体因素为网页加载时间以及后续元素(如图像、脚本和文本)的响应能力。SpeedCurve 隐私政策
Qualified
Qualified is the Autodesk Live Chat agent platform. This platform provides services to allow our customers to communicate in real-time with Autodesk support. We may collect unique ID for specific browser sessions during a chat. Qualified Privacy Policy

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改善您的体验 – 使我们能够为您展示与您相关的内容

Google Optimize
我们通过 Google Optimize 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Google Optimize 隐私政策
ClickTale
我们通过 ClickTale 更好地了解您可能会在站点的哪些方面遇到困难。我们通过会话记录来帮助了解您与站点的交互方式,包括页面上的各种元素。将隐藏可能会识别个人身份的信息,而不会收集此信息。. ClickTale 隐私政策
OneSignal
我们通过 OneSignal 在 OneSignal 提供支持的站点上投放数字广告。根据 OneSignal 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 OneSignal 收集的与您相关的数据相整合。我们利用发送给 OneSignal 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. OneSignal 隐私政策
Optimizely
我们通过 Optimizely 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Optimizely 隐私政策
Amplitude
我们通过 Amplitude 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Amplitude 隐私政策
Snowplow
我们通过 Snowplow 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Snowplow 隐私政策
UserVoice
我们通过 UserVoice 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. UserVoice 隐私政策
Clearbit
Clearbit 允许实时数据扩充,为客户提供个性化且相关的体验。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。Clearbit 隐私政策
YouTube
YouTube 是一个视频共享平台,允许用户在我们的网站上查看和共享嵌入视频。YouTube 提供关于视频性能的观看指标。 YouTube 隐私政策

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定制您的广告 – 允许我们为您提供针对性的广告

Adobe Analytics
我们通过 Adobe Analytics 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Adobe Analytics 隐私政策
Google Analytics (Web Analytics)
我们通过 Google Analytics (Web Analytics) 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Google Analytics (Web Analytics) 隐私政策
AdWords
我们通过 AdWords 在 AdWords 提供支持的站点上投放数字广告。根据 AdWords 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AdWords 收集的与您相关的数据相整合。我们利用发送给 AdWords 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AdWords 隐私政策
Marketo
我们通过 Marketo 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。我们可能会将此数据与从其他信息源收集的数据相整合,以根据高级分析处理方法向您提供改进的销售体验或客户服务体验以及更相关的内容。. Marketo 隐私政策
Doubleclick
我们通过 Doubleclick 在 Doubleclick 提供支持的站点上投放数字广告。根据 Doubleclick 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Doubleclick 收集的与您相关的数据相整合。我们利用发送给 Doubleclick 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Doubleclick 隐私政策
HubSpot
我们通过 HubSpot 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。. HubSpot 隐私政策
Twitter
我们通过 Twitter 在 Twitter 提供支持的站点上投放数字广告。根据 Twitter 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Twitter 收集的与您相关的数据相整合。我们利用发送给 Twitter 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Twitter 隐私政策
Facebook
我们通过 Facebook 在 Facebook 提供支持的站点上投放数字广告。根据 Facebook 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Facebook 收集的与您相关的数据相整合。我们利用发送给 Facebook 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Facebook 隐私政策
LinkedIn
我们通过 LinkedIn 在 LinkedIn 提供支持的站点上投放数字广告。根据 LinkedIn 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 LinkedIn 收集的与您相关的数据相整合。我们利用发送给 LinkedIn 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. LinkedIn 隐私政策
Yahoo! Japan
我们通过 Yahoo! Japan 在 Yahoo! Japan 提供支持的站点上投放数字广告。根据 Yahoo! Japan 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Yahoo! Japan 收集的与您相关的数据相整合。我们利用发送给 Yahoo! Japan 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Yahoo! Japan 隐私政策
Naver
我们通过 Naver 在 Naver 提供支持的站点上投放数字广告。根据 Naver 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Naver 收集的与您相关的数据相整合。我们利用发送给 Naver 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Naver 隐私政策
Quantcast
我们通过 Quantcast 在 Quantcast 提供支持的站点上投放数字广告。根据 Quantcast 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Quantcast 收集的与您相关的数据相整合。我们利用发送给 Quantcast 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Quantcast 隐私政策
Call Tracking
我们通过 Call Tracking 为推广活动提供专属的电话号码。从而,使您可以更快地联系我们的支持人员并帮助我们更精确地评估我们的表现。我们可能会通过提供的电话号码收集与您在站点中的活动相关的数据。. Call Tracking 隐私政策
Wunderkind
我们通过 Wunderkind 在 Wunderkind 提供支持的站点上投放数字广告。根据 Wunderkind 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Wunderkind 收集的与您相关的数据相整合。我们利用发送给 Wunderkind 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Wunderkind 隐私政策
ADC Media
我们通过 ADC Media 在 ADC Media 提供支持的站点上投放数字广告。根据 ADC Media 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 ADC Media 收集的与您相关的数据相整合。我们利用发送给 ADC Media 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. ADC Media 隐私政策
AgrantSEM
我们通过 AgrantSEM 在 AgrantSEM 提供支持的站点上投放数字广告。根据 AgrantSEM 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AgrantSEM 收集的与您相关的数据相整合。我们利用发送给 AgrantSEM 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AgrantSEM 隐私政策
Bidtellect
我们通过 Bidtellect 在 Bidtellect 提供支持的站点上投放数字广告。根据 Bidtellect 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bidtellect 收集的与您相关的数据相整合。我们利用发送给 Bidtellect 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bidtellect 隐私政策
Bing
我们通过 Bing 在 Bing 提供支持的站点上投放数字广告。根据 Bing 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bing 收集的与您相关的数据相整合。我们利用发送给 Bing 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bing 隐私政策
G2Crowd
我们通过 G2Crowd 在 G2Crowd 提供支持的站点上投放数字广告。根据 G2Crowd 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 G2Crowd 收集的与您相关的数据相整合。我们利用发送给 G2Crowd 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. G2Crowd 隐私政策
NMPI Display
我们通过 NMPI Display 在 NMPI Display 提供支持的站点上投放数字广告。根据 NMPI Display 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 NMPI Display 收集的与您相关的数据相整合。我们利用发送给 NMPI Display 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. NMPI Display 隐私政策
VK
我们通过 VK 在 VK 提供支持的站点上投放数字广告。根据 VK 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 VK 收集的与您相关的数据相整合。我们利用发送给 VK 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. VK 隐私政策
Adobe Target
我们通过 Adobe Target 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Adobe Target 隐私政策
Google Analytics (Advertising)
我们通过 Google Analytics (Advertising) 在 Google Analytics (Advertising) 提供支持的站点上投放数字广告。根据 Google Analytics (Advertising) 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Google Analytics (Advertising) 收集的与您相关的数据相整合。我们利用发送给 Google Analytics (Advertising) 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Google Analytics (Advertising) 隐私政策
Trendkite
我们通过 Trendkite 在 Trendkite 提供支持的站点上投放数字广告。根据 Trendkite 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Trendkite 收集的与您相关的数据相整合。我们利用发送给 Trendkite 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Trendkite 隐私政策
Hotjar
我们通过 Hotjar 在 Hotjar 提供支持的站点上投放数字广告。根据 Hotjar 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Hotjar 收集的与您相关的数据相整合。我们利用发送给 Hotjar 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Hotjar 隐私政策
6 Sense
我们通过 6 Sense 在 6 Sense 提供支持的站点上投放数字广告。根据 6 Sense 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 6 Sense 收集的与您相关的数据相整合。我们利用发送给 6 Sense 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. 6 Sense 隐私政策
Terminus
我们通过 Terminus 在 Terminus 提供支持的站点上投放数字广告。根据 Terminus 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Terminus 收集的与您相关的数据相整合。我们利用发送给 Terminus 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Terminus 隐私政策
StackAdapt
我们通过 StackAdapt 在 StackAdapt 提供支持的站点上投放数字广告。根据 StackAdapt 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 StackAdapt 收集的与您相关的数据相整合。我们利用发送给 StackAdapt 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. StackAdapt 隐私政策
The Trade Desk
我们通过 The Trade Desk 在 The Trade Desk 提供支持的站点上投放数字广告。根据 The Trade Desk 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 The Trade Desk 收集的与您相关的数据相整合。我们利用发送给 The Trade Desk 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. The Trade Desk 隐私政策
RollWorks
We use RollWorks to deploy digital advertising on sites supported by RollWorks. Ads are based on both RollWorks data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that RollWorks has collected from you. We use the data that we provide to RollWorks to better customize your digital advertising experience and present you with more relevant ads. RollWorks Privacy Policy

是否确定要简化联机体验?

我们希望您能够从我们这里获得良好体验。对于上一屏幕中的类别,如果选择“是”,我们将收集并使用您的数据以自定义您的体验并为您构建更好的应用程序。您可以访问我们的“隐私声明”,根据需要更改您的设置。

个性化您的体验,选择由您来做。

我们重视隐私权。我们收集的数据可以帮助我们了解您对我们产品的使用情况、您可能感兴趣的信息以及我们可以在哪些方面做出改善以使您与 Autodesk 的沟通更为顺畅。

我们是否可以收集并使用您的数据,从而为您打造个性化的体验?

通过管理您在此站点的隐私设置来了解个性化体验的好处,或访问我们的隐私声明详细了解您的可用选项。