{"id":15881,"date":"2020-06-17T03:04:19","date_gmt":"2020-06-17T07:04:19","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/simcenter\/?p=15881"},"modified":"2025-12-17T13:06:44","modified_gmt":"2025-12-17T18:06:44","slug":"post-processing-cfd-with-bubble-plots","status":"publish","type":"post","link":"https:\/\/blogs.stage.sw.siemens.com\/simcenter\/post-processing-cfd-with-bubble-plots\/","title":{"rendered":"You will never regret time spent blowing bubbles"},"content":{"rendered":"\n

If I tell you bubbles, what you would you say<\/a>? Probably not post-processing CFD results, right? The first thing that comes to my mind is fun! I picture myself as a kid blowing bubbles and staring at them flying. I remember following their trajectories and hoping them to last as long as possible. That\u2019s funny how such a simple thing could amaze me back in the days. And most of all, still amaze any kid in today\u2019s high-technology world. It really comes down to children discovering the beauty of physics without knowing it! I was far from imagining that a couple of decades later, I would use another form of bubbles in a totally different context. A sort of bubbles that would make me super excited all over again and nostalgic at the same time.  <\/p>\n\n\n\n

Bubble plots in Simcenter STAR-CCM+ v2020.2 are a new way of analyzing and communicating your results. It allows you to add multiple layers of information on a single XY plot when post-processing CFD results. This way, you can easily digest complex sets of information to better understand your product behavior. And this not only for a single simulations, but also for design exploration studies.   <\/p>\n\n\n\n

You will not believe how many bubbles one design can blow  – until you are post-processing CFD results<\/strong><\/h3>\n\n\n\n

The four plots below nicely illustrate the progression from a regular 2D plot to an advanced, multi-dimensional one using bubble plots for a rotating fan case. The first plot is nothing more than a regular XY plot where tangential velocity and pressure are proudly highlighting their relationship. By switching to a bubble plot style, we can add a third dimension to our data set. That\u2019s where the radial velocity enters the arena with the help of the bubble size as illustrated in the second plot. But what am I hearing? The temperature is now jealous and claims that it is the most important game player. It decided to force the entrance to defend its position! Fortunately, we have the bubbles\u2019 color to avoid a diplomatic incident! <\/p>\n\n\n\n

This way, we can now serenely analyze the relationship between our four different players in the third plot and identify trends in our product behavior. And if we are interested in only part of the information, data focus is our friend here! It will help us filter the data based, for example, on vorticity magnitude. This way we can focus on how prevalent high vorticity is on the fourth bubble plot.  <\/p>\n\n\n\n

\"post-processing
1. Pressure versus tangential velocity 2. Bubbles’ size adds radial velocity <\/figcaption><\/figure>\n\n\n\n
\"post-processing
3. Bubbles’ color adds temperature 4. Data focus in bubble plots <\/figcaption><\/figure>\n\n\n\n

Bubble plots are also flexible enough to be scaled using linear (default), log or area. For log or area, any negative value in the data source will be visually represented by a circle with an inscribed X\u200b. You also have the choice to hide these negative values if needed.  <\/p>\n\n\n\n

\"\"
Negative data values need special handling if            Data with negative symbol sizing can be log or square root size scaling is used         hidden to improve plot appearance 
 <\/figcaption><\/figure>\n\n\n\n

Better understand the outputs of transient simulation with bubble plots<\/strong><\/h3>\n\n\n\n

Another use for bubble plots is to better understand the outputs of transient simulation with complex physics. The video below illustrates a helicoidal mixer with a non-Newtonian fluid inducing mechanical stress that can cause failures. The objective here is to understand where and why our system could break. For that, we monitor the Von Mises stress and the stress mean values. <\/p>\n\n\n\n