DARPA recently announced the DARPA Lift Challenge, a competition intended to push drones and vertical-lift aircraft performance well beyond what is considered practical today. The challenge carries a total prize purse of $6.5 million, reflecting how seriously DARPA is treating the technical ambition behind the effort.

The goal is to demonstrate an aircraft configuration that can achieve roughly a 4:1 payload-to-weight ratio. To put that in perspective, most multirotor drones today operate much closer to a 1:1 ratio, where the vehicle can lift payload comparable to its own weight at best.

A 4:1 payload-to-weight target pushes vertical-lift design into a part of the performance envelope where rotor interference, wake interaction with the airframe, propulsion efficiency, and geometric packaging all become tightly coupled. Small layout changes can swing lift efficiency and stability in meaningful ways, and design intuition alone breaks down at this level of performance.

Why Flexcompute Flow360 Is Well Suited for Heavy-Lift Drone Design

To evaluate the large space of possible configurations required to approach a 4:1 payload-to-weight target, teams need more than raw solver speed. They need an end-to-end workflow that removes friction at every stage of the simulation loop, from geometry ingestion and meshing, physics setup, rotor modeling, and large-scale design sweeps. Flow360 is built around an automated end-to-end workflow for external aerodynamics and rotating machinery problems, which maps directly to the challenges of heavy-lift drone and VTOL design.

Flow360 is designed to move cleanly from early concept evaluation through aerodynamic database generation and into more detailed validation of critical flight conditions. For early-stage layout studies, the Web UI minimizes setup time so teams can evaluate configuration changes without rebuilding their workflow. As designs mature, the Python API supports automated case generation and post-processing across angles of attack, rotor speeds, and configuration variants. As this workflow scales, the choice of rotor modeling approach becomes a primary lever for controlling both physical fidelity and computational cost.

Rotor Modeling Across the Design Process

Flow360 supports a range of rotor modeling approaches to help teams balance the tradeoff between speed and fidelity as they scale their design workflows. There are five models available, including actuator disk, blade element theory disk and line, moving reference frame simulations, and fully resolved, time-accurate rotating blades.

• Actuator Disk: Represents the rotor as a distributed momentum source without resolving blade geometry. Best suited for early layout screening, overall thrust sizing, and understanding wake interaction trends at low cost.

• Blade Element Theory (BET) Disk: Steady blade element formulation that incorporates blade geometry and airfoil data. Useful for building aerodynamic trends across operating points while maintaining high throughput.
  

• Blade Element Theory (BET) Line: Unsteady blade element formulation that captures azimuthal variation and time-dependent effects without fully resolving blade geometry. Provides more realistic rotor behavior for transient and interaction-sensitive cases.
  

• Moving Reference Frame (MRF): Explicitly meshes the blades within a rotating reference frame to capture blade–flow interaction effects at higher fidelity than blade element models, without time-accurate blade motion.
  

• Fully Resolved, Time-Accurate Rotating Blades: Resolves blade geometry and time-accurate rotation using sliding interfaces. Best suited for detailed validation of wake development, blade–airframe interactions, and unsteady loading in critical flight conditions.

Building Aerodynamic Databases at Scale

Heavy-lift design depends on aerodynamic datasets that span operating conditions, rotor speeds, and configuration variants. Flow360 is designed to generate these datasets efficiently by combining automated workflows with multi-fidelity rotor modeling.

Lower-cost rotor models can be used to evaluate large numbers of configurations and operating points, mapping trends across the design space and narrowing in on promising regions. Higher-fidelity approaches can then be applied selectively to a smaller subset of designs to validate performance and capture interaction effects that lower-fidelity models cannot resolve. This tiered workflow makes it practical to build dense, physically credible aerodynamic databases without applying high-fidelity compute to every configuration.

GPU-Native Performance as the Throughput Multiplier

All of these workflow capabilities are built on Flow360’s GPU-native solver architecture. GPU acceleration shortens turnaround times for both individual cases and large parameter sweeps, which directly affects how much of the design space a team can cover within a fixed schedule. When combined with automated geometry handling, meshing, physics setup, and multi-fidelity rotor modeling, GPU-native performance becomes the multiplier that makes large-scale design exploration and aerodynamic database generation practical for heavy-lift drone concepts.

Supporting DARPA Lift Challenge Teams

The DARPA Lift Challenge demands serious engineering exploration across unconventional configurations and complex rotor–airframe interactions. Flow360 is built for this class of problem, supporting everything from early concept evaluation to large-scale aerodynamic database generation and targeted high-fidelity validation.

Flexcompute is offering a dedicated program for Lift Challenge participating individuals and teams who need a powerful CFD platform to explore unconventional designs, build credible aerodynamic datasets, and validate performance at scale.

Ready to validate your most ambitious designs? Reach out today to see how Flexcompute can help support your team and help you win the $6.5 million prize.