Non-Intrusive Computational Method and Uncertainty Quantification Tool for Isolator Operability Calculations

Computational fluid dynamics (CFD) technology plays a strong role in the design and development of aerospace and defense vehicles such as high-speed applications where testing under the correct operational conditions is not yet viable. Despite decades of research towards making CFD predictive and reliable, it has not proven so due to the significant uncertainties in physical models, initial/boundary conditions, computational mesh, numerical schemes and methods. In the proposed effort CFDRC in partnership with Virginia Tech and UTSI, aims to directly address these issues by integrating dimensionally adaptive sparse grid uncertainty quantification (UQ) method with an existing reacting CFD solver. The proposers demonstrated this approach to be suitable for achieving this objective during Phase I on a NASA-LaRC nozzle-isolator lab-scale setup. The proposed effort will deliver a practical user-friendly automated software tool combining UQ with CFD (UQCFD), capable of identifying and characterizing regions of high-uncertainty in the CFD code and the associated work-flow, and thereby, provide guidance to the CFD modeler to increase fidelity of those regions. UQCFD software has the potential to make significant impact on a wide variety of application utilizing CFD predictions including design and development of next generation supersonic and hypersonic flight vehicles.

Non-intrusive uncertainty quantification, that does not require the CFD code to be modified, has been identified as an enabling technology by NASA to advance the role of computational fluid dynamics codes in the Design Development Research and Engineering community developed by the aerospace industry, ultimately leading to utilization for flight certification. The proposed computational product offers a direct solution to link the various sources of uncertainties to predictions made by CFD tools, thereby enabling the usability of CFD tools for making risk-informed design decisions. The adaptive sparse grid method offers a significant advantage over other uncertainty quantification methods due to the ability to handle non-smooth system response with complex probability density distributions and much smaller number of required CFD simulations. This product can be a highly effective tool for wider applications requiring aerothermodynamics calculations where the lack of confidence in modeling parameters and predictive capability of the CFD codes has limited their impact.

The work established in this project including the Uncertainty Quantification workflow process and automated software tool can be generalized for potential applications to a wide range of applications utilizing CFD software. More specifically, this work can be transitioned to support a significant number of other non-NASA applications where reacting CFD modeling tools are utilized. Energy and propulsion applications such as gas-turbine combustors, augmentors, rockets, and many others can benefit from the product developed in the proposed work.

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