Sizing Analysis: Military Aircraft
This extended section of the book on military aircraft sizing analysis can be found on the Web at www. cambridge. org/Kundu and includes the following:
Figure 11.5. Aircraft sizing – military aircraft
11.7.1 Single-Seat Variant in the Family of Aircraft Design
Figure 11.6. Variant designs in the family of military aircraft
11.5 Sensitivity Study
The sizing exercise offers an opportunity to conduct a sensitivity study of the physical geometries so that designers and users have better insight in making finer choices. An example of an AJT wing-geometry sensitivity study is in Table 11.7 showing what happens with small changes in the wing reference area, SW; aspect
Table 11.7. AJT sensitivity study
ASir — ± 1.0 m
A Mach = ±0.004
A V touch do*v
AF = ±4.35 mph (3.78 kts)
Baseline two-seat trainer aircraft design.
Next variant: single-seat, uprated engine, lighter material, higher weapon load, advanced avionics on same LRUs.
Next variant: newwing, reheat engine basic FBW, highly manueverable.
Next variant: new cranked delta wing, new advanced engine, latest avionics. Fully FBWfor air-superiority role.
Same as above but with alternative design having canard wing and structural changes.
ratio, AR; aerofoil t/c ratio, t/c; and wing quarter-chord sweep, Л14. (A Bizjet aircraft sensitivity study is not provided in this book.)
A more refined analysis could be made with a detailed sensitivity study on various design parameters, such as other geometrical details, materials, and structural layout, to address cost-versus-performance issues in order to arrive at a satisfying design. This may require local optimization with full awareness that global optimization is not sacrificed. Although a broad-based MDO is the ultimate goal, dealing with a large number of parameters in a sophisticated algorithm may not be easy. It is still researched intensively within academic circles, but the industry tends to use MDO conservatively, if required in a parametric search, by addressing one variable at a time. The industry cannot afford to take risks with an unproven algorithm simply because it bears promise. The industry takes a more holistic approach to minimize costs without sacrificing safety, but it may compromise performance if it pays off.