Distributed Electric Propulsion (DEP) technology uses multiple propulsors driven by electric motors distributed about the airframe to yield beneficial aerodynamic-propulsioninteraction. The NASA SCEPTOR flight demonstration project will retrofit an existing internal combustion engine-powered light aircraft with two types of DEP: small "high-lift" propellers distributed along the leading edge of the wing which accelerate the flow over the wing at low speeds, and larger cruise propellers located at each wingtip for primary propulsive power. The updated high-lift system enables a 2.5x reduction in wing area as compared to the original aircraft, reducing drag at cruise and shifting the velocity for maximum lift-to-drag ratio to a higher speed, while maintaining low-speed performance. The wingtip-mounted cruise propellers interact with the wingtip vortex, enabling a further efficiency increase that can reduce propulsive power by 10%. A tradespace exploration approach is developed that enables rapid identification of salient trades, and subsequent creation of SCEPTOR demonstrator geometries. These candidates were scrutinized by subject matter experts to identify design preferences that were not modeled during configuration exploration. This exploration and design approach is used to create an aircraft that consumes an estimated 4.8x less energy at the selected cruise point when compared to the original aircraft. Nomenclature = coefficient of drag 0 = coefficient of drag at zero lift = coefficient of lift = maximum coefficient of lift ⁄ = ratio of drag to dynamic pressure = battery specific energy = energy use per unit distance, conventional configuration = energy use per unit distance, distributed electric propulsion configuration = aircraft gross weight ℎ = altitude above mean sea level = induced drag constant ⁄ = ratio of lift to drag ( ⁄ ) = maximum ratio of lift to drag = mass of battery pack = power consumption of aircraft at cruise = specific excess power (instantaneous rate of climb capability) = rate of descent = range parameter at cruise power, no reserves = efficiency multiplier = velocity at cruise 0 = stall speed in the landing configuration , ∞ = airspeed velocity

A computational study of the wing for the distributed electric propulsion X-57 Maxwell airplane configuration at cruise and takeoff/landing conditions was completed. Two unstructured-mesh, Navier-Stokes computational fluid dynamics methods, FUN3D and USM3D, were used to predict the wing performance. The goal of the X-57 wing and distributed electric propulsion system design was to meet or exceed the required lift coefficient 3.95 for a stall speed of 58 knots, with a cruise speed of 150 knots at an altitude of 8,000 ft. The X-57 Maxwell airplane was designed with a small, high aspect ratio cruise wing that was designed for a high cruise lift coefficient (0.75) at angle of attack of 0°. The cruise propulsors at the wingtip rotate counter to the wingtip vortex and reduce induced drag by 7.5 percent at an angle of attack of 0.6°. The unblown maximum lift coefficient of the high-lift wing (with the 30° flap setting) is 2.439. The stall speed goal performance metric was confirmed with a blown wing computed effective lift coefficient of 4.202. The lift augmentation from the high-lift, distributed electric propulsion system is 1.7. The predicted cruise wing drag coefficient of 0.02191 is 0.00076 above the drag allotted for the wing in the original estimate. However, the predicted drag overage for the wing would only use 10.1 percent of the original estimated drag margin, which is 0.00749.

A computational study of a distributed electric propulsion wing with a 40° flap deflection has been completed using FUN3D. Two lift-augmentation power conditions were compared with the power-off configuration on the high-lift wing (40° flap) at a 73 mph freestream flow and for a range of angles of attack from -5 degrees to 14 degrees. The computational study also included investigating the benefit of corotating versus counter-rotating propeller spin direction to powered-lift performance. The results indicate a large benefit in lift coefficient, over the entire range of angle of attack studied, by using corotating propellers that all spin counter to the wingtip vortex. For the landing condition, 73 mph, the unpowered 40° flap configuration achieved a maximum lift coefficient of 2.3. With high-lift blowing the maximum lift coefficient increased to 5.61. Therefore, the lift augmentation is a factor of 2.4. Taking advantage of the fullspan lift augmentation at similar performance means that a wing powered with the distributed electric propulsion system requires only 42 percent of the wing area of the unpowered wing. This technology will allow wings to be 'cruise optimized', meaning that they will be able to fly closer to maximum lift over drag conditions at the design cruise speed of the aircraft.

A variety of tools, from fundamental to high order, have been used to better understand applications of distributed electric propulsion to aid the wing and propulsion system design of the Leading Edge Asynchronous Propulsion Technology (LEAPTech) project and the X-57 Maxwell airplane. Three highfidelity, Navier-Stokes computational fluid dynamics codes used during the project with results presented here are FUN3D, STAR-CCM+, and OVERFLOW. These codes employ various turbulence models to predict fully turbulent and transitional flow. Results from these codes are compared for two distributed electric propulsion configurations: the wing tested at NASA Armstrong on the Hybrid-Electric Integrated Systems Testbed truck, and the wing designed for the X-57 Maxwell airplane. Results from these computational tools for the high-lift wing tested on the Hybrid-Electric Integrated Systems Testbed truck and the X-57 high-lift wing presented compare reasonably well. The goal of the X-57 wing and distributed electric propulsion system design achieving or exceeding the required " = 3.95 for stall speed was confirmed with all of the computational codes. Nomenclature Symbols # drag coefficient a angle of attack, degrees #,%&'()* drag coefficient, pylons contribution Δ delta #,,-). drag coefficient, wing contribution #,/0 drag coefficient, tip nacelles contribution Acronyms #,10" drag coefficient, high-lift nacelles contribution BSL Menter kbasic turbulence model " lift coefficient CFL pseudo time advancement Courant-Friedrichs-Lewy ",344 effective lift coefficient: " + ",%5(% DEP distributed electric propulsion ",678 maximum lift coefficient HLN high-lift nacelles ",%5(% lift coefficient from the contribution of propeller thrust in lift direction KCAS KEAS knots calibrated airspeed knots equivalent airspeed 6

The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler's purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized