The modeling tools are often used in conjunction with numerical design of experiments (DOE) to reduce modeling effort and to gain more insight into design parameter interactions. Often the optimal solution isn’t the numerical optimal, but the optimum within an insensitive region based on manufacturing tolerances and noise variables. My personal philosophy is to use numerical modeling as digital experiments, often run in conjunction with lab experiments. The experiments confirm the models and the models inform the experiments.

Note that due to the proprietary nature of the work done within GEHC, only high-level details can be shared.

Types of CFD Modeling at GEHC

CFD modeling spans the range from simple, such as pneumatic pressure drops or temperature limited electronics cooling problems, to the complex, such as precise temperature control problems or compressible flow studies through valves. Additionally, time depend transient studies are often needed in modalities such as respiratory and anesthesia care (ARC).

Below is a summary of simulation uses within GHEC:

• Concept Development: Map Design option for NPI’s
• Design Optimization: Hone selected concepts
• Design Implementation: Optimize system trade offs
• Root Cause Analysis: Understand field failure/Test results

Modeling Examples From GEHC

Electronics Cooling Example – FloEFD w/ Electronics Cooling Module

This is a typical electronics cooling problem. In this case, a new system on chip module was replacing an obsolete processor, and a new heat sink cooling solution that could fit within the existing space needed to be developed. To solve this problem, the SOLIDWORKS CFD package with the electronics cooling module was utilized to design the heat sink blower solution.

Figure 1. Example of a Typical Electronics Cooling Problem.

Figure 2. Example results.

This example demonstrates use of 2R chip models, PWB tool and flow and surface plots post processing of the temperature and flow results.

Valve Characterization Example

This effort consisted of mapping the performance of a high-speed fluid pulse width modulation (PWM) controlled injection valve as a function of inlet pressure, temperature and fluid. The results were used to determine the correct valve geometry, PWM duty cycles and the controls plant model of the valve, (partial results shown in Figure 3). The model established the dynamic range capability versus inlet pressure and fluid type, and the impact of noise variables such as temperature, pressure fluctuations and valve opening time.

Figure 3. Some results from high speed CFD modeling.

Ultrasonic Flow Sensor Design

The initial request was to evaluate a design that was developed experimentally. Empirical development led to poor performance and lack of understanding of what was driving it. CFD simulations were used to identify issues with flow field uniformity and transient flow pulsations. Ultimately, a new design was proposed and quantified numerically and experimentally.

Figure 4. Overview of ultrasonic flow sensor simulation project.

Figure 5. Sample CFD results from Ultra Sonic flow sensor.

In Figures 5 and 6, it can be seen that the flow fields are non-uniform spatially (steady state), and temporally (transient) with large difference between the minimum and maximum flows (0.15lpm to 15lpm).

Figure 6. Overview of transient analysis of ultrasonic flow sensor.

A new design was proposed where the flow was brought in around the perimeter of the ultrasonic transducers. The passageways around the perimeter were gridded to simulate a “honeycomb” structure to eliminate flow pulsations. As can be seen in Figure 8, the flow fields are very uniform, which was confirmed experimentally.

Figure 7. Design developed through CFD modeling of Ultra sonic flow sensor.

Particle Study Capability

In this example, a CFD particle study was used to understand how injected liquid would be entrained by flowing gas and how both fluids would be heated in a mixing chamber.

Figure 8. An example of using particle study.

This model was used to gain qualitative flow interaction knowledge, which helped guide the inlet and mixing design.

Case Study Example of Design Space Mapping and Use of Numerical DOE

CT Detector Temperature Control System Development Example – VCT

In the early 2000’s, GE designed a revolutionary 64 slice CT system from the ground up, which presented significant temperature control design challenges. The heat generating from the A to D electronics needed to be packaged near the temperature sensitive photodiode and scintillator of the x-ray sensor. The available cooling air DT was limited, as the maximum electronic temperature was close the maximum T_air. Additionally, an altitude of 0-3500m had to be accommodated.

A CT scan cycle consists of rotating from rest to high speed in just a few seconds, which caused a simultaneous rise in T_air and a change in air velocity near the detector (convective boundary condition shift). For artifact-free imaging, the x-ray sensor needs to remain essentially constant throughout a scan cycle. To solve this problem an architecture was proposed, then CFD/electronics modeling was used to map the design space and drive the design details.

These details included the obvious, such as number of fans, heat sink geometry and ducting, to the non-obvious such as placement of the chips, underfilling of chips, circuit board copper layout and flex effective thermal conductivity, to name a few.

Pictures of the exterior and interior of the GE 64 slice scanner are given in Figure 9. In the right-most picture of Figure 9, the system is rotating at maximum speed, which translates to a linear velocity of ~35mph near the x-ray sensors. The transition from stationary to final rotation speed takes only a few seconds, resulting in a large transient shift in convective boundary condition.

Figure 9. GE VCT 64. (Images taken from public domain.)

System Architecture – Start of the Design Space Mapping

CFD modeling was initiated to both investigate feasibility and to drive the design decisions. Figure 10 shows a sketch of the system and lays out the noise and design variables along with design outputs. The approach from architectural concept, critical variable identification, modeling methodology and sample of numerical DOE results are shown in Figures 11-14.

Figure 10. Sketch of system air flow path.

To start, a simple network representation (figure 11) was developed and used to drive the numerical DOEs. A global local modeling approach was used, where flow resistances from local models (numerical wind tunnel studies) were used to obtain global model flow boundary conditions, which were in turn fed into local models. Detailed electronics cooling models were used to determine steady state and transient temperatures of key components. From the DOE studies, transfer functions were built and used to map the design space, significantly reducing the number of computational runs and providing key insight into variable interactions.  

Figure 11. Node Network representation.

Figure 12. Global-Local methodology.

Figure 13. Example of DOE results.

Once the design space and component interactions were mapped, detail optimization began.

Figure 14. Example of Numerical Optimization and experimental and CFD Data.

Summary

A combination of CFD (with electronics cooling module), conduction heat transfer modeling using global local modeling technique driven by numerical DOE were used to show and drive design feasibility. The modeling effort took approximately nine months and was confirmed experimentally with a ¼ system bench model, followed by a system prototype. Prototype to production consisted of controls algorithm development only.

To learn more about SOLIDWORKS simulation for product development in health care, check out the whitepaper Simulating for Better Health and the webinar Free Surface Flow Simulation at GE Healthcare.

Setting Up the Model

We will start with a simple model of water flow in a pipe. This is a model I used when I was evaluating various CFD packages. It is a 100-ft horizontal length of 4-in-diameter pipe. It’s nothing exciting but a result I knew I could verify from having done these calculations manually and from other technical references (first rule of simulation, trust but verify!).

Figure 1. Solid model of pipe geometry.

 Figure 1 shows a simple solid model of a hollow cylinder to represent our pipe. The next step is to bring this model to the flow environment. The easiest way of doing this is to use the Flow Simulation Wizard. With the SOLIDWORKS Flow Simulation add-in activated, the display should appear similar to Figure 2.

Figure 2. Flow Simulation menu ribbon.

Selecting the wizard option from the menu brings up the dialog box shown in Figure 3.

Figure 3. Flow Simulation Wizard dialog box.

Clicking “Next,” the dialog box in Figure 4 appears for unit system selection. Conveniently, we can mix and match units. This is particularly useful when performing a flow/thermal simulation using U.S. units. We will continue with the default IPS units.

Figure 4. Unit System Wizard dialog box.

The next dialog is the analysis type, as shown in Figure 5. The type of analysis can usually be determined intuitively. Internal flow is bound by a solid at the flow outer boundary. Our current model is internal and the fluid is bound by the pipe walls. An external flow example would be airflow over an airplane wing.

Figure 5. Selecting the analysis type.

We then add the fluid we are simulating to the project. Selecting water in Figure 6 adds it to the project fluids section as the default fluid.

Figure 6. Defining the project fluids.

After completing the wizard, we are greeted with our first error, as seen in Figure 7.

Figure 7. Error message due to nonwatertight model.

Yes, the pipe must actually have its ends “sealed” and be watertight to be able to simulate flow through it. All flow simulation must happen over some contained volume—the “fluid volume.” The software does not know where to end the problem if we don't cap the ends, so we select “Yes” in the dialog box. We are then asked to select the open ends that we need to close, and lids are created as shown in Figure 8. The purpose of the lids and closing the model will be clearer when we discuss boundary conditions.

Figure 8. Adding lids to the pipe ends.

Boundary Conditions

Prior to placing boundary conditions, we basically have a water bottle. The model requires boundary conditions to define the inlets and outlets. However, prior to defining them, we will perform a model check using the Tools → Flow Simulation →Tools → Check Geometry command. This checks that the model has valid geometry to proceed with the analysis. We can also enable a Show Fluid option, which gives us the graphic shown in Figure 9.

Figure 9. The fluid volume.

Referring to Figure 9, we make the following definitions:

• This is the boundary referred to as the computational domain. It represents the mathematical boundary of the flow problem. For internal flow, it closely, if not identically, corresponds to the fluid volume. At a minimum, it must envelop the fluid volume.
• This is the fluid region/volume that the software recognized. It is important to note that this is the only volume in the model that the analysis is concerned with. The solid bodies (pipe wall and lids) are there as a convenient way of defining boundary conditions. They do not participate in the flow simulation.
• This is the lid that we added to close the ends of the pipe. It does not participate in the analysis but serves as a reference to define boundary conditions, as we will see next.

Applying Boundary Conditions

Boundary conditions are where we define inlets and outlets for the flow. For our problem, we know the flow rate and are interested in flow distribution and overall pressure drop. Figure 10 shows the explorer pane for the Flow Simulation environment.

Figure 10. Flow Simulation explorer pane.

Right-clicking on the Boundary Conditions item brings up the dialog box in Figure 11.

Figure 11. Boundary Condition dialog box.

The face that we select for the boundary is the interior of the lid we added to seal the model. We are not able to select the fluid directly. The software assigns those lid surface conditions to the fluid in contact with that face (see Figure 9). If the lid face is not coincident with the fluid, there will be an error applying the boundary condition.

We now have an inlet flow (aka volumetric flow rate) defined as 40 ft3/min. We need to define an outlet by putting a pressure boundary condition at the other end of the pipe, again at the internal lid face. As shown in Figure 12, the outlet pressure is defined as ambient (101 kPa or 14.7 psi).

Figure 12. Defining the outlet conditions.

With these boundary conditions, the problem statement can be written as follows: “Calculate the pressure required to move 40 ft³/min of water through a 4-in-diameter pipe, 100 ft long, with an open discharge.”

Finally, we add goals to the model. Goals give the solver guidance on our solution objective. In this case, we add the inlet pressure as a goal. This is done by right-clicking “Goals” in Figure 10 and inserting a surface goal on the inlet lid face. This is the same face we used to define the inlet flow.

Meshing

The fluid volume is meshed into a grid for the simulation to proceed. This is analogous to the mesh in finite element analysis. The default automatic mesh settings work very well for most flow simulation problems. However, even in the default automatic mode, there are refinement options available to the user. Figure 13 shows the dialog for setting mesh refinement from a scale of 1 to 7. The mesh preview is immediately updated in the model.

Figure 13. Mesh/grid settings.

With the problem properly defined and bound, we are ready to run the simulation. The Tools → Flow Simulation → Solve → Run command brings up the dialog in Figure 14.

Figure 14. Run dialog box.

We will choose “New calculation” and select “Run.” If there was a previous partial analysis performed, there is an option to continue the calculation so as to not have to rerun prior iterations.

The analysis progress and convergence can be monitored by selecting a goals plot. For our analysis, we are solving for the inlet pressure and we graph the convergence over time/iterations. This is useful to troubleshoot convergence issues. Figure 15 shows the value of the inlet pressure over the final iterations of the simulation.

Figure 15. Convergence plot.

Postprocessing

There are many options for querying and viewing the results. Figure 16 shows the various graphics and result quantities that can be evaluated.

Figure 16. Results options.

Two of the most common are Flow Trajectories and Surface Parameters. For our problem, we are interested in a surface parameter, namely the pressure required at the inlet face/surface of the pipe to flow 40 ft³/min of water 100 feet. Right-clicking on the Surface Parameters option brings up the dialog box in Figure 17. We select the face of the lid to represent the inlet surface of the fluid and select “Show” to bring up the results in Figure 18.

Figure 17. Getting inlet pressure result.

Figure 18. Listing of calculated inlet pressure.

The results show that the average inlet pressure is 136,102 Pa. The outlet was set at ambient (101,325 Pa), giving a pressure drop of 35 kPa (5 psi).

The flow trajectories for our simple pipe model are essentially straight lines. Figure 19 better illustrates a flow trajectory plot. It’s taken from a sample model that combines flow and thermal effects.

Figure 19. Sample flow trajectory plot.

Thermal Analysis

The flow model can be easily converted to a thermal model. We will modify the previous model to simulate a water heater by setting the pipe temperature to 500 °F and determining the water outlet temperature. Going to the General Settings dialog box and selecting “Wall conditions” brings up the dialog shown in Figure 20. We set the pipe wall to 500 °F (533 K).

Figure 20. Setting pipe wall temperature.

The steps to obtain a solution are the same as in the previous flow simulation.
The result we are interested in is the water discharge temperature to determine the amount of heat the 500 °F pipe transmits to the water. We right-click on the surface parameters item in Figure 16, which brings up the dialog box in Figure 21.

Figure 21. Water outlet temperature.

The outlet lid face is selected as the reference surface over which the water temperature will be evaluated.

Figure 22. Heat Transfer Coefficient parameter.

Figure 21 shows the results, indicating an average water discharge temperature of 316 K (109 °F). Other heat transfer–related parameters, such as the heat transfer coefficient (HTC), can also be determined. Figure 22 shows the results from selecting “Heat Transfer Coefficient” and the interior face of the pipe wall.
This shows that the average HTC acting at the pipe/water interface is 19,252 W/m²-K (3,390 Btu/hr/ft²-°F).

Conclusion

SOLIDWORKS Flow Simulation 2016 has capabilities for solving various flow and thermal problems. It uses the SOLIDWORKS modeling engine to define the physical geometry, and then the Flow Simulation environment defines boundary conditions and examines simulation results. In this article, we set up and solved a flow problem from a solid model through analysis and post-processing. The model was then converted to include thermal effects through a simple boundary condition change on the pipe wall. We also evaluated results pertinent to both flow and thermal simulations.

About the Author

Attilio Colangelo has more than 25 years of experience in engineering and project management in the chemical, process, ceramic and advanced-materials industries. His specialties include CAE, with an emphasis on FEA, high-temperature and heavy industrial design. His software skills include SOLIDWORKS Simulation, NASTRAN, Caesar II, ANSYS and iOS programming.