Drone Flight Time Calculator

Physics-based flight time from battery, mass, rotors and altitude.

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Knowing how long your drone will fly before landing — or before the battery is damaged by over-discharge — is critical whether you are planning a survey grid, a cinematic orbit, or just an afternoon freestyle session. This calculator uses actuator-disk theory, the same aerodynamic framework behind helicopter and wind-turbine design, to give you a physics-grounded estimate rather than a rough rule of thumb.

How it works

Every multirotor hovers by pushing air downward. The power needed to do that is governed by the actuator-disk (momentum) model, derived from conservation of momentum and energy in the air column beneath each rotor:

P_ideal = sqrt( T³ / (2 · ρ · A_total) )

where T is hover thrust (equal to the drone’s all-up weight in newtons), ρ is air density at flight altitude, and A_total is the combined disk area of all rotors. Real propellers are not perfect actuator disks, so actual hover power is higher by a factor called the figure of merit (FM):

P_hover = P_ideal / FM

FM typically runs 0.65–0.82 for hobby props, depending on blade profile and pitch. The calculator estimates FM from your thrust-to-weight ratio.

Air density is computed from the ISA (International Standard Atmosphere) troposphere formula, which accounts for the temperature lapse rate of 6.5 K per 1000 m. This matters: at 2000 m above sea level, air density is roughly 10% lower than at sea level, requiring proportionally higher RPM and power for the same thrust.

Flight time combines that hover power with your battery energy:

T_flight (min) = ( C_mAh × V × η / 1000 ) / P_avg × 60

where C_mAh is battery capacity, V is nominal pack voltage, η is discharge efficiency (usable fraction), and P_avg is average power during the flight. Because power scales as thrust to the power of 1.5 (from momentum theory), average power at a given throttle percentage is:

P_avg = P_hover × (throttle% / 50%)^1.5

This correctly predicts the super-linear power spike when you push a drone to full throttle.

Worked example — DJI-class 1.5 kg quad

A consumer-grade quadcopter with these specs:

ParameterValue
Battery5000 mAh, 22.2 V (6S LiPo)
All-up mass1500 g
Rotors4 × 22.86 cm (9-inch)
Altitudesea level
Discharge efficiency80%
Average throttle50% (calm hover)

Step-by-step:

  1. Disk area: 4 × π × (0.1143 m)² = 0.1642 m²
  2. Hover thrust: 1.5 kg × 9.807 = 14.71 N
  3. P_ideal = sqrt(14.71³ / (2 × 1.225 × 0.1642)) = 89.0 W
  4. FM ≈ 0.82 (TWR = 2, long-endurance props) → P_hover = 89.0 / 0.82 = 108.5 W
  5. Battery energy: (5000 / 1000) × 22.2 = 111 Wh; usable: 111 × 0.80 = 88.8 Wh
  6. P_avg at 50% throttle = P_hover × 1.0 = 108.5 W
  7. T_flight = (88.8 / 108.5) × 60 ≈ 49 minutes

That is consistent with physics-based estimates for similarly spec’d long-range platforms. Add wind (raise throttle to 65%) and estimated time drops to around 33 minutes — consistent with real-world pilot reports for moderate-wind conditions.

Key variables and their impact

VariableEffect on flight time
Battery capacity (mAh)Linear — double the mAh, double the time
Rotor diameterStrong positive — larger disk = lower power
All-up massStrong negative — heavier = more thrust needed
AltitudeNegative — thinner air forces higher RPM
Average throttleHighly non-linear via the 1.5 power law
Discharge efficiencyLinear — 85% vs 75% is a 13% swing

The solve-for feature inverts the flight-time equation to tell you exactly how many mAh you need to hit a target duration — useful when sizing a battery for a specific mission before you buy.

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