aerodynamics

DJI showcases a slowed 60x lab test of the Mavic 4 Pro's hovering rotors, revealing steady tip vortices that demonstrate efficient airflow management for stability and control.

This paper presents Robostreet Flow, a freight architecture combining a lightweight battery-electric tractor with a four-truck hybrid convoy system that reduces operating costs by 56% compared to diesel baselines through aerodynamic drag reduction, lightweighting, and driver amortization.

This paper presents a novel approach combining Graph Neural Networks with augmented Neural Ordinary Differential Equations (GNODE) for stable and accurate spatio-temporal prediction of unsteady airfoil aerodynamics, outperforming autoregressive baselines on transonic shock and non-linear dynamics tests.

A detailed technical article explaining drone physics, including coordinate systems, equations of motion, forces, and control, with references to the multirotor simulation framework.

New research on the 2026 World Cup's Trionda ball, Adidas's first four-panel design, shows its unique aerodynamic properties affect trajectory and drag, with potential game impact.

This newsletter covers the aerodynamics of the 2026 World Cup ball and OpenAI's plans to turn ChatGPT into a 'super app' before its IPO, along with other tech news such as Google's deal with SpaceX and Trump's proposal for US government stake in AI companies.

Researchers tested the new Adidas Trionda World Cup ball and found it may slightly reduce distance on long kicks but rewards clean technique and predictable flight.

This paper proposes AirfoilGen, a latent diffusion model for airfoil shape generation that ensures geometric validity via a circle sweeping representation and enables control over aerodynamic performance (lift/drag coefficients). Experiments show 98.41% performance-conditioning accuracy, using a new dataset of over 200,000 airfoils.

This paper introduces AeroJEPA, a Joint-Embedding Predictive Architecture for scalable 3D aerodynamic field modeling. It addresses limitations in current surrogate models by predicting semantic latent representations of flow fields, enabling efficient high-fidelity analysis and design optimization.

A 2019 blog post from FLOW Lab at BYU explores how to optimize Julia code to match C++ performance using a real-world aerodynamics application (vortex particle method) as a benchmark. The author shares lessons learned about achieving high-performance computing in Julia through type declarations, JIT compilation, and code optimization techniques.