WrightOne was founded in 2020 in Massachusetts, to develop better energy efficient electric turbines. Their revolutionary design is currently the most energy efficient model on the market. Currently they service the eVTOL, aerospace turbine, commercial turbine and electric propulsion verticals.

Challenge: Optimizing Turbine Fan Airflow

Solution: Parallel Pipes' physics-informed-AI & Generative Design-with-Primitives

Results: 175% Airflow Increase

Next Steps

CHALLENGE:

Optimizing Turbine Fan Airflow

The product development team at Wright One encountered a few major challenges when designing a cooling solution using their energy efficient electric turbines. Their main priority was to improve the blade profile to increase airflow performance. Their secondary goals were to reduce mass and increase lifespan of the design.

• Increase airflow to ≥150 CFM
• Ensure manufacturability of blades (additive manufacturing)
• Decrease cyclic stress leading to fatigue damage

Figure 1:   WightOne’s cooling solution

Wright One’s design team has used Parallel Pipes for several analysis jobs before. They decided to use Parallel Pipes to improve the performance of their turbine, to ensure their product had several clear advantages over their competitors. WrightOne’s turbine is already one of the most energy efficient on the market today; having better airflow would make them the clear winner in the eyes of customers.

“We got a full-fledged solution and generative design that actually took the physics of our problem into account [CFD, multiphysics & rotordynamics]. As an early stage company, resource constraints are always hindering product development and we are always overloaded with projects. With Parallel Pipes we were given the ability to offload the most time-consuming parts and expensive aspects of R&D development of the design work and move forward on other projects was invaluable.”

- Ralph Sieja, Principal Mechanical Engineer, WrightOne Inc.

SOLUTION:   

Parallel Pipes AI, using Physics-based Generative Design-with-Primitives

Optimizing turbine blades for performance has too many variables to easily rely on the standard computer-in-the-loop solution used by modern design teams today. Using human engineers to verify the results every iteration takes too long when there are more than one or two revisions. Their team turned to Parallel Pipes to maximize their turbine’s airflow using the Aerodynamic Efficiency Workflow, Parallel Pipes' proprietary physics-informed AI, and Generative Design-with-Primitives.

Figure 2:   Turbine, initial model

“With Parallel Pipes we no longer feel like we are taking risks with our development. We can explore alternative design pathways and find the best design that fulfills our product requirements.”

- Justin McAfee, CEO, WrightOne Inc.

After uploading the initial model, the design priorities were set. First was to increase airflow to ≥150 CFM using the Aerodynamic Efficiency workflow. Additional design priorities were to reduce mass and decrease fatigue & cyclic stress.

Practically speaking, this meant increasing airflow as much as possible, with a minimum requirement of 150 CFM. On top of this, the mass and cyclic stress leading to fatigue would be reduced as long as they did not interfere with the main goal.

The initial blade design performed adequately, but didn’t reach the goal of 150 CFM. To surpass it the AI used the simulation results to determine how to best modify the design.

Over several iterations the AI improved the airflow by changes to the blade geometry and configuration, verified with simulation results, and repeated until no further improvements were found.

The final design had 175% more airflow than the initial design. On top of the superior performance the mass and cyclic stress were slightly reduced, leading to less fatigue and longer lifespan. The cost reduction from having less material was offset by a slightly more complex manufacturing process.

RESULTS:   

175% Airflow Increase

Wright One’s design team was able to use Parallel Pipes to significantly improve their cooling solution. The final design had 175% more airflow than the initial design. On top of the superior performance, the mass and cyclic stress were reduced leading to less fatigue and longer lifespan. The cost reduction from less material was offset by a slightly more complex manufacturing process.

After an initial walkthrough of the platform by the Parallel Pipes support staff, WrightOne’s design team was able to successfully set up and run an Aerodynamics Optimization Workflow, specify design goals and identify the blades to be optimized using the physics-informed-AI and Generative Design-with-Primitives. This ensured the final design would be manufacturable using traditional techniques, rather than additive techniques such as 3D printing.

After building the prototype, the turbine fan blades were found to perform according to the results from the optimization job.

The final design had 175% more airflow than the initial design. On top of the superior performance, the mass and cyclic stress were reduced leading to less fatigue and longer lifespan. The cost reduction from less material was offset by a slightly more complex manufacturing process.

“We got a full-fledged solution and generative design that actually took the physics of our problem into account [CFD, FSI & rotordynamics]. As an early stage company, resource constraints are always hindering product development and we are always overloaded with projects. With Parallel Pipes we were given the ability to offload the most time-consuming parts and expensive aspects of R&D development of the design work and move forward on other projects was invaluable.”

- Ralph Sieja, Principal Mechanical Engineer, WrightOne Inc.

NEXT STEPS:   

Best High Performance & Energy Efficient Turbine on the Market

After their success with Parallel Pipes, WrightOne plans to run analysis workflows with Parallel Pipes in order to plan out the next iteration of their cooling system design. Once the core design is decided upon, they will subsequently use the platform for optimization in order to find the best design for their needs.