Learning CFD from Afar

This blog was contributed by Garrett Clyma, CFD Engineer at Flow Science.

Passion Meets Opportunity

It’s difficult for a high school student to predict a specific career path, especially in the broad world of engineering. At that stage in my life, all I knew was that I was good at math, loved science, and had an interest in space travel, largely in part to the booming developments of SpaceX and the anticipation of the James Webb Space Telescope. Majoring in aerospace engineering was the obvious choice, prompting me to move across my home state to attend Western Michigan University.

As I progressed through my degree, I saw for the first time that there was more to solving engineering problems than just design, manufacturing, and process engineering. I enjoyed social situations and interacting with other students, which furthered my interest away from a purely technical engineering career. Although learning about space will always remain a hobby, my time in school and at internships caused me to think outside the realm of aero- and astronautics to a career in which I could combine my love for science and problem solving, while still using my social skills. I was introduced to CFD for the first time through my senior capstone project, which concentrated on the design of a scramjet combustion test lab. Using software to simulate the supersonic flow within the wind tunnel sparked my interest in numerical modeling and had me questioning, “where else is CFD used?”

It just so happens that, from a combination of luck and opportunity, I was able to land a post-graduate remote internship with Flow Science in a career path I was extremely interested in and could picture myself excelling in. This internship offered me a three-month opportunity to increase my competitiveness for a full-time position.

Strategic Objectives

CFD is, simply put, complicated. How was I, a college graduate with sparse CFD experience, to become well versed enough to solve and convey complex topics for highly technical clients in just 3 months? My manager, John Wendelbo, and I laid out a plan with two objectives.

  • Objective #1: Develop my CFD modeling skills to be competitive for a Sales Engineer position
  • Objective #2: In the process, fine-tune my presentation skills and develop a strong familiarity with value propositions, sales pipelines, and the inner workings of sales group processes.

To accomplish my objectives, we set a generalized plan for me to learn the FLOW-3D product family, spending about 3 weeks on each software. The idea was to work from lower to higher complexity CFD concepts and continue a steady buildup of aptitude in modeling while approaching the software like any new user, by utilizing the extensive directory of user training materials. This would give me a strong base of CFD knowledge while also providing plenty of opportunities to pick the brains of my colleagues, asking relevant questions when needed (and irrelevant questions when curious) to accomplish my second objective.

Working remotely from a different state had implications that I had yet to experience in a working environment. The lack of face-to-face interaction with my co-workers could have had a negative impact on relationship-building and productivity. To combat that possibility, I introduced myself to each of my colleagues over Zoom throughout my internship and allowed them to put a face to a name and exhibit my positive, motivated, and curious approach to work. I organized myself by creating daily, weekly, and monthly goals to circle back to and adjusted my work habits to be as productive at home as I would be in an office.

Onward and Upward

Due to the broad design of my internship, I was exposed to an array of industries, physics concepts, and software features rather than focusing on one or two specific applications. Since similar physics phenomena are present across different engineering problems, learning a range of applications provided me with a more thorough understanding of which physics models are important to include and which are not for an effective simulation. Modeling microfluidic capillary flows brought me insight to surface tension physics which I could apply to melt pool modeling in laser powder-bed fusion and bubble formation in boiling water. Additionally, setting up simulations to validate experimental research let me practice creating models of real situations which included exploring the changing flow rate over a labyrinth hydraulic weir and spillway and the effect of gravity on electron beam penetration of a metal disc.

Garrett Clyma attends Rapid tct 2022
Garrett (left) with coworker Ibai Mugica (right) at the RAPID+TCT Additive Manufacturing Tradeshow in Detroit, MI

The variety in my experience continued when I got to leave my “home office” for a few days to attend the RAPID+TCT 3D Printing and Additive Manufacturing Conference in Detroit, MI alongside two colleagues that had flown in. The energy there was high and foot traffic at our booth reflected that. I enjoyed speaking to academics, engineers, executives, and investors about what they are looking for in applications relating to CFD respective to their roles in the business or engineering process. Walking the conference floor exposed me to different companies’ capabilities and trends present within the industry, both of which will help me make informed decisions in my role.

It’s fun for me to look back and think about how I arrived at this stage of my academic and professional career and there’s no shortage of people to thank. I’m now very happy to say that, after a successful internship, I’ve been hired full-time as a CFD Engineer with the sales team focusing on additive manufacturing and metal casting applications.

Optimization of a Sand Casting with FLOW-3D CAST

Optimization of a Sand Casting with FLOW-3D CAST

This blog was contributed by Malte Leonhard, Flow Science Deutschland.

The goal of a casting simulation is to predict casting defects and find the corrective measures to avoid such defects. To achieve this, the casting simulation must accurately predict the physical results. In this article, we illustrate a validation of FLOW-3D CAST by comparing simulation results with experimental findings. For this validation, we selected an aluminum casting with significant porosity defects. The extent of the porosities meant that the casting would not fulfill the functional specification and would be rejected.

Optimization of casting design and runner/riser system supported by simulation

In order to verify that FLOW-3D CAST can reliably predict such casting defects, the existing casting was simulated and the results compared with the real casting defects. This video shows that the FLOW-3D CAST defect analysis correctly reproduced the defects and their position.

The simulation results suggested that reversing the casting direction might be beneficial, so the casting part was rotated by 180° in the mold. Next, the runner and riser system were modified so that most of the risers were integrated into the runner (“hot risers”).

Additionally, design changes of the casting, which we will discuss later in the article, were introduced in order to achieve a directed solidification to the risers and to avoid porosities in the casting.

Finally, chills were applied in areas of large mass accumulations to reduce shrinkage defects.

A filling and solidification simulation showed that these measures significantly reduced the casting defects. These simulation results were again validated by X-rays. Near the “cold risers”, those not integrated into the runner system, not all defects could be avoided due to the risers’ insufficient size.

In the last version, the two cold risers were enlarged. Minor design changes of the casting were introduced to improve the directional solidification to the risers.

Compared to the original design, the casting quality was substantially improved and the amount of return scrap reduced. The use of chills improved the quality in critical areas within the casting. Overall, the casting process is now more efficient and cost-effective for the foundry.

Total sprue material3,73 kg(-3,3 kg)
Pouring weight8,93 kg (-3,19 kg)
Yield58 %(+24%)
Chills1,53 kg(+1,26 kg)

Design modifications of the casting

In many cases it is not sufficient to optimize the runner and riser system to achieve a sufficient casting quality. Instead, the design of the casting itself needs to be changed for the sake of “castability”.

The functional design of a casting often includes mass accumulations that prove problematic in the casting process. To achieve a high-quality microstructure, the wall thickness should always increase towards the risers according to the thermal modulus method.

To illustrate the effect of the casting design modifications, two geometry versions with identical runner and riser systems are compared. The adjustments are shown in the figure below. Several wall thicknesses and ribs between material accumulations have been enlarged.

For demonstration purposes, the two marked ribs will be examined. In both cases, porosities occur at the junctions, which is not acceptable.

By increasing the wall thicknesses of the ribs, the junctions are connected to the feeders adjacent to the casting edge and a directed solidification toward the risers is achieved.

The video shows large porosities in the initial version on the left side due to large hot spots within the casting where solidification-induced shrinkage creates a deficit.

In the optimized version on the right, the melt solidifies in the direction of the risers. This means that the deficit can be compensated by the riser and no porosity is created in the casting.

This validation shows a good correlation between reality and simulation in FLOW-3D CAST. The software is an effective tool for the development of castings, casting systems, feeder dimensions/shapes, and casting processes to ensure optimal quality and efficiency.

FLOW-3D (x): Connect, Automate, Optimize

FLOW-3D (x): Connect, Automate, Optimize

In this blog, we’ll look at FLOW-3D (x) – a completely new product from Flow Science that will change the way you work with FLOW-3D products, make you more productive, improve your designs beyond what you thought was possible, and give you a deeper insight into your simulations than ever before. First, we’ll talk about how users typically incorporate simulation into their workflow and where bottlenecks often occur. Then we’ll talk about how FLOW-3D (x) removes these bottlenecks by automating the entire user workflow. And then we’ll look at some actual projects that were completed with FLOW-3D (x). FLOW-3D simulations provide users with the ability to predict how their designs will behave without building expensive prototypes. Many combinations of parameters and geometry can be simulated to find the optimal design. However, simulating many designs to achieve the optimal behavior can be time and cost prohibitive when done manually. And there is no guarantee that the solution achieved is the best since there is usually no simple way to know the relationship between parameter changes and design performance since we’re choosing the parameters’ values blindly.

Running Parametric Geometry Designs

A common scenario is to have a parametric geometry designed in CAD. To understand the effect of geometry changes on the performance of the design, the user has to modify the geometry in CAD, export the geometry to STL, run the simulation, then postprocess the results. The number of design alternatives that can be investigated in this way is very limited due to the time required. Additionally, it is often useful to examine the behavior of a design through a range of fluid properties. If we wanted to investigate the results of viscosity varying over a range of values, we’d have to modify the input files for each value we’d like to simulate, execute each, and then postprocess. This way of working can quickly become prohibitively time consuming. The solution is to use FLOW-3D (x) to automate this iterative testing process.

Optimization Workflow

The first step in creating an optimization workflow in FLOW-3D (x) is to define the goal of the optimization. The goal may be to minimize or maximize a simulation output (e.g., air entrainment) or some statistical value such as the average flow rate that is computed using the Statistics plugin. Next, the parameter space to be examined is specified along with the possible range these parameters can assume in the optimization. There is no limit to the number of parameters that can be studied. Finally, a Budget is defined which tells FLOW-3D (x) how many simulations it can execute in its search for an optimal solution. The larger the simulation budget, the closer the solution will be to the actual optimal solution.

Connecting & Automating

A wide range of plugins are available which allow almost any workflow to be replicated and automated:

  • SolidWorks
  • Catia
  • NX
  • PTC Creo
  • Rhino Grasshopper
  • SpaceClaim
  • Autodesk Inventor
  • Abaqus
  • Matlab
  • Math/statistics
  • Excel

The STL Morpher CAD plugin allows us to automate the typically tedious task of opening our CAD geometry, modifying it, and then exporting it to the simulation directory in a FLOW-3D (x) workflow. For example, let’s say we had a parameterized design of a pipe network in SolidWorks. We’d like to study the effect of a change in a particular pipe diameter on the flow rate through the pipe. To automate this, we would drag a SolidWorks plugin into our workflow in FLOW-3D (x), open the SolidWorks part file in the SolidWorks plugin, and then select the diameter as a variable we’d like to control in our optimization. Then we’d specify the allowable range of this diameter. FLOW-3D (x) will run a series of simulations with geometries of various diameters generated though the SolidWorks plugin. No interaction between the user and the software is necessary. We could have FLOW-3D (x) identify the optimal diameter which minimizes or maximizes some flow quantity such as turbulent kinetic energy, for example.

Below is an example of a workflow created in FLOW-3D (x) that uses the STL Morpher plugin to modify the STL geometry of a manifold to achieve a balanced flow through each distribution pipe of the manifold. The manifold is shown here.

With the drag-and-drop feature in the FLOW-3D (x) interface, this type of workflow can be set up and running in minutes.

Each time a workflow cycle is completed, the new data is added to the response surface, further refining the relationship between the inputs and the outputs. Based on the computed response surface, a new set of inputs is created by FLOW-3D (x) and another cycle of the workflow is executed. This cycle repeats until the optimization goal is achieved or the user-specified budget is reached.

A natural output from this process is a sensitivity plot which indicates how strongly the simulation results depend on the inputs. For example, we’d typically be interested in knowing whether a particular simulation parameter is worth optimizing. If its effect on the results is minimal, we know that we need to look at some other parameter in the simulation to improve our design. The sensitivity graphs below show the standard deviation of the flow rates through the manifold outlets on the vertical axis and the variations in the outlet diameter. The interaction is strong for all three, indicating they all contribute significantly to the results and are indeed what we should be considering.

The sensitivity graphs shown here show the standard deviation of the flow rates through the manifold outlets on the vertical axis and the variations in the outlet diameter. The interaction is strong for all three, indicating they all contribute significantly to the results and are indeed what we should be considering.

Workflow Automation

Aside from optimization and parameter sensitivity studies, FLOW-3D (x) can also be used for workflow automation without performing any optimization. For example, if we simply wanted to run a series of simulations with a specified set of inputs and then create a set of post-processed results, we could do that. In that case, we would define a CSV file with the inputs we’d like to simulate (e.g., viscosity, turbulence model selection, mesh size, inlet velocity) and execute these simulations automatically.

As you can see, using FLOW-3D (x) alongside any FLOW-3D product makes you more productive, provides more in-depth clarity about your design, and allows you to get the most value possible from your CFD workflow.

John Ditter

John Ditter

Principal CFD Engineer at Flow Science

Exploring Ray Tracing in FLOW-3D POST

Exploring Ray Tracing in FLOW-3D POST

In the world of CFD, we use FLOW-3D POST to learn about our processes, analyze, visualize, and communicate the results of our simulations. We can now use the ray tracing option to increase the quality of our images and videos. While the ray tracing tool may not be necessary for every day simulation work, it is a great tool for presentation and outbound materials. Simulations can now be rendered with realistic-looking materials, depth of field, and lighting, and shadows to better engage viewers and guide them to the most interesting aspects of the results.

What is Ray Tracing?

The rendering engine works by setting up a geometric scene. First, we need a scene object or component. This will be our 3D simulation model that exists in a 3D dimensional space. Next, we choose a virtual camera or viewpoint, which will define the perspective of our image. Third, we need an image plane, which will be perpendicular to our viewing direction. The image plane is divided into pixels and is where the final image will be constructed. User:Henrik    CC BY-SA 4.0

The ray tracing engine can now start filling in the pixels on the image plane. View rays are constructed from the camera viewpoint, through the center of each pixel on the image plane, and then extend out into to the scene object. When the ray intersects our object, the color of the intersection point is determined and sent back to its respective pixel in the image plane. This process is done for every pixel in the image plane to construct an image.

Shadows and Lighting

Another variable we can specify in our ray tracing calculation is lighting. Light sources will affect the shade and intensity of the colors sent to the image plane from our view ray. This effect is defined mathematically by the rendering equation, which was first introduced by James Kajiya from CalTech in 1986. For a basic understanding, the equation can be broken down into three factors. Keeping these factors in mind will help guide you as you start incorporating light sources into your simulation.

1. How much light is incoming or falling on our scene object?

Here we can consider the intensity of our light source, the distance from the light source to the scene object, and the orientation of the scene object with respect to the light source.

2. How does the surface of our scene object reflect the light?

This is where we can consider the material of our object. Different materials have different reflectivity. For instance, materials can be shiny like water or dull like concrete. In FLOW-3D POST we have many material definitions ready to use. Note that dull objects are defined as diffuse and shinier objects are defined as specular.

3. Where is the location of your camera or viewpoint?

Moving the viewing angle will influence the perceived light reflection.

Ray Tracing in FLOW-3D POST

With a component selected, select Enable Ray Tracing in the Properties section. From here you can use either the OSPray Raycaster or OSPray Pathtracer engines for rendering. The path tracing approach is considered a more accurate representation of lighting and illumination, but this calculation can be more computationally intensive.

Now you can start exploring the ray tracing back end. You can experiment with materials from the pre-loaded options or define them from an imported material library:

  • Adjust camera properties such as focal point and depth of field using the adjust camera panel
  • Background settings can be defined as backplate or environment 

We have had success using the backplate to define a ground or floor to the object. Finally, the light inspector can be used to set up your light sources; this includes area lights for softer shadows.

With this overview of ray tracing, you now can start incorporating ray tracing and light sources to create photo-realistic renderings of your simulation results. We will be following up this post with some of our suggested settings that our team likes to use for different applications.

Stay tuned for our next blog on the animation view and keyframing features in FLOW-3D POST.

At Flow Science we develop innovative solutions that help our customers conceptualize, create, and analyze their simulations with confidence. If you would like more information or a personal demonstration of FLOW-3D POST, please send an email to webdemo@flow3d.com.

Thank you and stay tuned for our next post!

Ajit D'Brass

Ajit D'Brass

Metal Casting Engineer at Flow Science

Communicate Your CFD Results with Confidence

The ability to visualize and present clear, meaningful analysis of simulation data is a crucial part of the CFD simulation process. FLOW-3D POST provides all the necessary tools to effectively communicate your simulation findings. In this blog, we’ll explore some powerful features of FLOW-3D POST so that you can communicate your CFD results with confidence.

Here is an example of an aluminum wheel casting, simulated using the low pressure die casting workspace in FLOW-3D CAST. The objective of this simulation is to find the steady state thermal condition within the mold. Thermal die cycling is used to visualize mold performance and ensure that there is a proper thermal gradient in the mold before beginning the filling simulation. This image shows a few easily accessible features in FLOW-3D POST.

Using multiple viewports allows the user to get a complete visualization to examine areas and features of interest. This is also useful when wanting to compare multiple designs. Notice that the top left viewport is using a 3-dimensional slice; 2D and 3D slicing options allow the user to visualize the internal features of geometries and flow systems.

The bottom viewports show a graphical output of history data based on the thermal energy transferred to the mold components.

FLOW-3D POST features
Aluminum wheel casting, simulated using the low pressure die casting workspace in FLOW-3D CAST.

The legend and annotation properties are fully customizable, allowing you to label and display your data clearly and effectively. Additional features shown here include a customized color scale and opacity setting for the mold visualization.

FLOW-3D POST CFD results

As we go deeper into the functionality of FLOW-3D POST we see some powerful ways to extrapolate information. In this tailings breach example, we can see the use of a spreadsheet data view on the right. Here, data from individual cells can be highlighted and isolated, helping the user illustrate flow features at a mesh cell-level of precision. In the panel on the left, we can see the calculator function, which can be used to manipulate history data on the fly. If quick decision-making is important to the project, built-in capabilities such as exporting data for statistical analysis can provide macro- and micro-level viewpoints of the results efficiently. 

Stay tuned! We will be diving into some of the new features including ray tracing, cell-level data extrapolation, and key framing. In the meantime, go to our FLOW-3D POST product page to see some of examples of how powerful of a tool FLOW-3D POST is.

 

At Flow Science we develop innovative solutions that help our customers conceptualize, create, and analyze their simulations with confidence. If you would like more information or a personal demonstration of any of our FLOW-3D products and FLOW-3D POST, please contact us at webdemo@flow3d.com.

Ajit D'Brass

Ajit D'Brass

Metal Casting Engineer at Flow Science

FLOW-3D POST ray tracing CFD results
As a teaser for our next post, here is a simulation image of a hydraulic jump produced with the ray tracing feature.

Join us for the What's New in FLOW-3D (x) 2022R1 webinar on August 25 at 1pm ET

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