Browsing by Author "Shahpar, Shahrokh"

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    Extending highly loaded axial fan operability range through novel blade design
    (American Society of Mechanical Engineers, 2022-09-19) Lopez, Diego I.; Ghisu, Tiziano; Kipouros, Timoleon; Shahpar, Shahrokh; Wilson, Mark
    The tip clearance size has historically been considered to be the main factor affecting stability range in axial fan and compressors. This paper reveals that the stall characteristics are defined by the axial momentum flux of the tip leakage flow and that tip clearance is primarily a strong driver for this metric. A bespoke methodology for carefully tailoring the axial momentum via three-dimensional design is presented, which enables a higher degree of control over the stability range for cases where the tip clearance responds to other considerations and cannot be defined for this purpose. The effect of the axial momentum on efficiency is also addressed and the trade-off between operability range and design point performance is derived. The results show that the conditions for optimal stability differ from those for optimal efficiency and that control over the axial momentum enables tuning the design for a desired exchange. Numerical simulations have been employed to drive the analysis through a high-fidelity computational model whose behavior is supported by rich set of experimental data. Contrary to current belief, results further indicate that an accurate characterization of stall, including onset mechanism, can be achieved through steady-state simulations, minimizing the need for expensive time-accurate computations during the design phase.
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    Gradient-enhanced least-square polynomial chaos expansions for uncertainty quantification and robust optimization
    (AIAA, 2021-07-28) Ghisu, Tiziano; Lopez, Diego I.; Seshadri, Pranay; Shahpar, Shahrokh
    Regression-based Polynomial Chaos expansions offer several advantages over projection-based approaches, including their lower computation cost and greater flexibility. In the presence of expensive function evaluations, such as with computational fluid dynamics and finite element analysis, the availability of gradient information, coming from adjoint solvers, can be used to reduce the cost of least-square estimation. Particular attention needs to be payed to the accuracy of gradient information, as adjoint solvers are often more noisy than their primal counterparts. This paper compares different approaches for gradient-enhanced least-square Polynomial Chaos expansion, both for algebraic test cases, and for real-world test cases, i.e. a transonic compressor and a modern jet engine fan.