SUAVE, a conceptual level aircraft design environment, incorporates multiple information sources to analyze unconventional configurations. Developing the capability of producing credible conceptual level design conclusions for futuristic aircraft with advanced technologies is a primary directive. Many software tools for aircraft conceptual design rely upon empirical correlations and other handbook approximations. SUAVE proposes a way to design aircraft featuring advanced technologies by augmenting relevant correlations with physics-based methods. SUAVE is constructed as a modular set of analysis tools written compactly and evaluated with minimal programming effort. Additional capabilities can be incorporated using extensible interfaces and prototyped with a top-level script. The flexibility of the environment allows the creation of arbitrary mission profiles, unconventional propulsion networks, and right-fidelity at right-time discipline analyses. This article will first explain how SUAVE's analysis capabilities are organized to enable flexibility. Then, it will summarize the analysis strategies for the various disciplines required to evaluate a mission. Of particular interest will be the construction of unconventional energy networks necessary to evaluate configurations such as hybrid-electric commercial transports and solar-electric unmanned aerial vehicles (UAVs). Finally, verification and validation studies will be presented to demonstrate the capabilities of SUAVE, including cases for conventional and unconventional vehicles. While some of these cases will be optimized results, discussion of SUAVE's interface with optimization will be reserved for a future publication.

SUAVE, a conceptual level aircraft design environment, incorporates multiple information sources to analyze unconventional configurations. Developing the capability to produce credible conceptual level design conclusions for futuristic aircraft with advanced technologies is a primary directive. This work builds upon previous work where SUAVE analyzed aircraft to show how SUAVE may be integrated into external packages to optimize aerospace vehicles.In the context of optimization, SUAVE operates as a "black-box" function with multiple inputs and multiple outputs. Several convenient functions are provided to enable connecting the optimization packages to SUAVE more easily. Assuming an optimization algorithm is minimizing an objective subject to constraints by iteratively modifying input variables, SUAVE's code structure is general enough to be driven from a variety of optimization packages. To this point, connections to PyOpt and SciPy have been integrated into SUAVE.We present results for a multi-mission regional aircraft, a family of UAVs and a tradeoff between noise and fuel burn on a large single-aisle aircraft. These designs show the immense amount of flexibility and diversity that SUAVE can handle. This includes various levels of fidelity. While SUAVE is setup from the beginning to handle multi-fidelity analysis, further study is necessary to integrate multiple fidelity levels into a single vehicle optimization.

Aircraft Conceptual Design is a challenging task that requires not only the understanding of many different disciplines, but also how they interact with each other, leading to many trade-off analyses. The complexity of these interactions grows fast with the number of variables, disciplines and goals of the problem. Multidisciplinary Analysis and Optimization tools can be very helpful to explore the design space, but it is up to the engineering team to define the objective function: optimal with respect to what? For commercial aircraft segment there are two well defined objectives that mostly represent what costumers of this segment desire: minimization of block fuel or minimization of direct operating costs. The objective function is not so clear for the Executive Jets segment and it is usual to apply minimization of MTOW. This paper proposes an architecture that includes Costs, Market Share and Finances disciplines in the optimization loop, treating the market specifications provided by company's intelligence as constraints and maximizes the financial return to the shareholders. This way, the conceived solution complies with all customer needs and also provides the most attractive investment to the shareholders. A comparison is made with the traditional optimization strategy and the results shows that, although both strategies are not exactly conflicting objectives, to maximize the financial return can lead to a different design with significant improvement in financial return. Another key optimization issue treated in this work is the reliability and robustness of the design. Estimation methods have inherent model uncertainties that can not be mitigated even with in-house data calibration. Uncertainty quantification and Robust Design is held in this work by the use of Monte Carlo simulations using triangular distributions and superposition of effects. Results for the Robust Optimization showed that slight changes in the design can improve robustness in the outcomes of interest. Also, the proposed methodology reduces the computational cost of the Robust Design to almost the level of a deterministic design, presenting a significant improvement to this process. Engine Inoperative RC = Rate of Climb Re = Reynolds number Ri = Range for the i-th mission Sref, Sw = Wing reference area SRi = Specific Range for the i-th mission t/c = Thickness-to-Chord ratio TLR = Top Level Requirements TO = Take-off TOFL = Take-Off Field Length TSFC = Thrust Specific Fuel Consumption TSLS = Engine's Sea-Level Static Thrust V, TAS = True Airspeed Vfuel = Fuel Volume VH = Horizontal Tail Volume coefficient VT = Vertical Tail VV = Vertical Tail Volume coefficient Wresf = Fuel reserves weight α, AoA = Angle of Attack β = Sideslip angle Λ = Sweep angle λ = Taper-Ratio Financial acronyms and symbols DOC = Direct Operating Costs IRR = Internal Rate of Return NPV = Net Present Value Optimization acronyms and symbols MDO = Multidisciplinary Design Optimization VDD = Value-Driven Design MOGA = Multi-Objective Genetic Algorithm φ(.) = Vector of O...

SUAVE is a conceptual level aircraft design environment that incorporates multiple information sources to analyze unconventional configurations. This work incorporates higherfidelity tools to build upon previous efforts where SUAVE analyzed and optimized several types of aircraft using low-fidelity methods. This is done in an automated way that incorporates three external programs. The first is OpenVSP, which is used for geometry creation, area calculation, and surface meshing. The second is Gmsh, which uses these surface meshes to create volume meshes. The third is SU2, which is used to run Euler CFD simulations. Wetted areas from OpenVSP and lift from SU2 is used to enhance SUAVE's aerodynamic analyses. We present results for a verification case with the Onera M6 wing, then present mission results with a conventional narrow-body airliner, a supersonic jet, and a blended wing body.

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