AeroQubIt

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AeroQubIt

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STAGE 1 · Geometric Modeling and Configuration Comparison

Farklı tasarım aşamalarındaki UAV uçak modelleri.

V0–V5 motor configurations (OpenVSP)

L/D aerodinamik verim değerlerini karşılaştıran grafik.

L/D comparison of V1–V5 configurations and References Platform.

STAGE 2 · Machine Learning-Based Design Optimization

ML model accuracy metrics and optimal design parameters.

SHAP analysis: relative effects of wing geometry parameters on CL, CD, and L/D.

SHAP analysis: relative effects of wing geometry parameters on CL, CD, and L/D.

SHAP analysis: relative effects of wing geometry parameters on CL, CD, and L/D.

SHAP analysis: relative effects of wing geometry parameters on CL, CD, and L/D.

SHAP analysis: relative effects of wing geometry parameters on CL, CD, and L/D.

Final OTW distributed electric propulsion surveillance platform.

SHAP analysis: relative effects of wing geometry parameters on CL, CD, and L/D.

Final OTW distributed electric propulsion surveillance platform.

STAGE 3 · CFD and Multidisciplinary Performance Analysis

Rotor-on configuration vorticity and pressure distributions (OpenVSP).

Comparison of rotor-off CFD, rotor-on CFD, and ML predictions.

Classical and quantum CFD runtime comparison.

Classical and quantum CFD runtime comparison.

STAGE 4 · Quantum-Assisted Flow Modeling

Section-wise accuracy of the quantum–classical hybrid model (K0–K9).

STAGE 5 · Experimental Studies

Prototype manufacturing stages: wing, fuselage, and system integration.

Wind tunnel test views of PLA-printed wing section model.

Final views of wind tunnel model and prototype aircraft.

Final views of wind tunnel model and prototype aircraft.

CONCLUSION

The study demonstrates that an over-the-wing (OTW) DEP architecture is technically feasible for surveillance-oriented electric aircraft. Combining a high-aspect-ratio wing with OTW rotor placement improves L/D and gives a clear advantage over existing tactical electric platforms. The integrated use of CFD, ML surrogate modelling and a quantum–classical hybrid expanded analysis scope while cutting computational cost —  the surrogate agrees with an independent CFD case to 1.0 %, and the hybrid model returns a section estimate about 720 times faster than solving the field (with some accuracy loss reflecting current NISQ limits). Beyond surveillance, the platform's low-noise, low-thermal-signature design suits civilian uses such as environmental monitoring, infrastructure inspection and search-and-rescue. Future work covers a differential ML architecture and validation in professional wind tunnels with university partners.


 © 2026 Batuhan Türk · İTO Science and Art Center 

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Welcome to the AeroQubit

This is a brief interactive preview rather than the full project, giving you a quick look at its key features and concepts. Enjoy exploring!