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• Predict lift/drag/moment/stability derivative/efficiency/performance/noise/emissions characteristics with certifiable accuracy over the complete flight envelope. Certifiable accuracy refers to the quality of the numerical results that would be acceptable for product certification (such as FAA certification of engine systems for icing and bird strike)

• Simulate steady-state and time-dependent flows including problems with dynamically deforming geometries and relative body motion including possible changes in topology (e.g., real-time high-lift system deployment, aeroelastic wing response, rotor/airframe interaction, store separation)
• Be applicable to all Mach number ranges from subsonic to hypersonic flows, from low to high Reynolds numbers
• Routinely simulate flows with smooth body separation, massive separation and other complex flow physics including chemically reacting flows
• Routinely model laminar to turbulent flow transition of all modalities (T-S waves, cross-flow and Görtler instabilities; natural and bypass
• Enable quantification of various error sources including discretization (both spatial and temporal), algebraic and modeling errors
• Provide automated capability for simulating to overall error tolerances
• Provide (as standard output) full quantification of numerical errors, sensitivity information, and computational uncertainty for specified quantities.

Technology Robustness. Technology robustness refers to elements and characteristics of the 2030 CFD software that are required to streamline, automate, and enable the effective use of CFD for both aerodynamic analysis and design. In terms of technology robustness, the Vision 2030 CFD capability must:

• Employ integrated, fully automated CAD incorporation, grid generation, and solution adaptive techniques for entire vehicle and propulsion simulations with minimal user intervention
• Provide an intuitive parameter free interface enabling optimal use for a wide range of problems while minimizing the required user learning curve.
• Accept both epistemic and aleatory probabilistic inputs and return suitable outputs for the purposes of quantifying uncertainty.
• Operate across multi-platform computing environments. This refers to the need to link together many separate analysis and design tools that reside on different platforms (when it is often not practical to convert or port these tools to one system).
• Be seamlessly integrated into visualization and data mining techniques that make full, efficient use of results from time-resolved, physically complex flowfield simulations.
• Provide flexible linkages with ground-based and flight test datasets to build integrated aerodynamic databases with prescribed confidence intervals throughout the database.
• Enable coupling with other disciplines and with flight control system simulations for steady and time dependent trim and maneuver simulations.
• Enable robust simulation capability across all flow regimes, in particular due to nonlinear and transient effects, without the need for users to perform application-specific tuning.
• Provide fault-tolerant simulation execution, particularly for use with aerodynamic optimization workflows.
• Enable the efficient construction of large aerodynamic databases with prescribed confidence intervals throughout the database.