Use of CFD & FEA Analysis at GE Health Care, Part 1: CFD
Within GE Health Care (GEHC), FEA and CFD modeling are used extensively in each modality, and the type of modeling and tools used are dependent on the unique challenges within that modality. While there is traditional analysis of a given design to check for safety factors or thermal margin, the real power is driving designs using FEA and CFD tools.
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 Tair. 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 Tair 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.
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.