Our Goals:
We seek the merger of innovative surface engineering methodologies, rapid prototyping, advanced manufacturing, and artificial intelligence (AI). The goal is the reduction of aerodynamic frictional drag and increased lift for subsonic to hypersonic systems for aerospace, energy, and transportation applications.
The fluid dynamics and aerodynamic advances are directly applicable to aeronautic motors, high-performance combustion, rockets, airfoils, drone blades, transport vehicles (air, sea, and land), and high-efficiency energy systems, to name a few. This is achieved through a seamless blending and leveraging of critical and emerging leading-edge technologies involving advanced turbulence theory, biomimicry, and AI.
Some anticipated end results include:
• Decreased fuel consumption due to reduced frictional drag
• Reduced CO2 emission; many green energy applications
• Increased rocket and aerospace vehicle payload
• Longer flight time for drones, projectiles, rockets
• Improved airfoil lift
• Lessened noise levels for rotors, blades, and airfoils
• Reduced flight signature
• Increased levels of heat transfer (because the boundary layer is sharper and has a higher velocity gradient near the wall)
Technical Approach
The engineered applications involve dimples, swirl, grooves, and fore and aft geometry refinements. The methodology leverages bio-mimetics, fundamental turbulence theory, AI, and advanced manufacturing, and wind tunnel testing, with the end goal of modifying the system’s boundary layer, induce early drag crisis for reduced frictional drag and increased aerodynamic lift, as well as the minimization of boundary layer detachment. Moreover, these advances are known to contribute to quieter systems, reduced flight signature, increased heat transfer, and increased component lifespan.
To speed up design development and performance validation, the continuous-improvement approach consists of the following:
1. Apply a novel set of turbulence equations to rapidly obtain a surface-engineered design
2. Apply AI to refine and optimize the system design
3. 3D-print the design
4. Test the device in a wind tunnel to obtain performance data (or use those from collaborating universities, such as the University of New Mexico or the University of Arizona)
5. If performance metrics are not yet met, repeat Task 1 and continue refining/optimizing the system design
Due to the streamlined, integrated approach where all necessary stages are available within a single location, the design-to-testing iteration for Tasks 1 through 4 can be completed within three days or less.
The fluid dynamics and aerodynamic advances are directly applicable to aeronautic motors, high-performance combustion, rockets, airfoils, drone blades, transport vehicles (air, sea, and land), and high-efficiency energy systems, to name a few. This is achieved through a seamless blending and leveraging of critical and emerging leading-edge technologies involving advanced turbulence theory, biomimicry, and AI.
Some anticipated end results include:
• Decreased fuel consumption due to reduced frictional drag
• Reduced CO2 emission; many green energy applications
• Increased rocket and aerospace vehicle payload
• Longer flight time for drones, projectiles, rockets
• Improved airfoil lift
• Lessened noise levels for rotors, blades, and airfoils
• Reduced flight signature
• Increased levels of heat transfer (because the boundary layer is sharper and has a higher velocity gradient near the wall)
Technical Approach
The engineered applications involve dimples, swirl, grooves, and fore and aft geometry refinements. The methodology leverages bio-mimetics, fundamental turbulence theory, AI, and advanced manufacturing, and wind tunnel testing, with the end goal of modifying the system’s boundary layer, induce early drag crisis for reduced frictional drag and increased aerodynamic lift, as well as the minimization of boundary layer detachment. Moreover, these advances are known to contribute to quieter systems, reduced flight signature, increased heat transfer, and increased component lifespan.
To speed up design development and performance validation, the continuous-improvement approach consists of the following:
1. Apply a novel set of turbulence equations to rapidly obtain a surface-engineered design
2. Apply AI to refine and optimize the system design
3. 3D-print the design
4. Test the device in a wind tunnel to obtain performance data (or use those from collaborating universities, such as the University of New Mexico or the University of Arizona)
5. If performance metrics are not yet met, repeat Task 1 and continue refining/optimizing the system design
Due to the streamlined, integrated approach where all necessary stages are available within a single location, the design-to-testing iteration for Tasks 1 through 4 can be completed within three days or less.
Sal Rodriguez