Browsing by Author "Sharma, Ankit"

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    Framework for multi-fidelity assessment of open rotor propeller aeroacoustics
    (AIAA, 2024-05-30) Huang, Guangyuan; Sharma, Ankit; Chen, Xin; Riaz, Atif; Jefferson-Loveday, Richard
    Aerodynamically generated noise from open rotor aircraft has received immense research interests. Multi-fidelity numerical approaches are in demand for evaluating open rotor propeller noise without compromising computational accuracy and reducing cost. In this paper, propeller noise modelling methods at different fidelity levels are assessed by application to an aircraft propeller configuration at an advance ratio of 0.485 together with tip Reynolds and Mach numbers of 3.7×10^5 and 0.231, respectively. The flow solution of the propeller is obtained using coarse-grid Large Eddy Simulation and then inputted into three acoustic solvers. At higher-fidelity level, Ffowcs-Williams and Hawkings analogy method is employed. Hanson’s method and Gutin’s method are applied at the medium- and lower -fidelity levels, respectively. Results from the three models are compared correlatively, as well as against existing experimental measurement data. Through the assessment, insight is given into future development of a multi-fidelity model for low-emission open rotor aircraft design. The presented multi-fidelity framework is being developed as part of the Innovate UK, Aerospace Technology Institute (ATI) funded research project – ONEheart (Out of Cycle NExt generation highly efficient air transport).
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    Requirements uncertainty propagation in conceptual design using bayesian networks
    (International Council of the Aeronautical Sciences (ICAS), 2024-09-09) Spinelli, Andrea; Sharma, Ankit; Kipouros, Timoleon
    This paper presents the application of a Bayesian Network as a tool for propagating the uncertainty between the aircraft-level design and the component-level design. The framework is applied to an example case in UAV design for payload transport. By querying the model, we demonstrate its ability to capture the casual relationships of the design problem and propagating the effects of design decisions on other parameters and requirements.