Mechanical Engineering - Research Publications
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ItemNo Preview AvailablePulsed impinging jets: Momentum and heat-transfer(PERGAMON-ELSEVIER SCIENCE LTD, 2022-05-15)
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ItemNo Preview AvailableMomentum boundary-layer characterisation from a pulsed impinging jet(ELSEVIER SCIENCE INC, 2022-04)
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ItemAn extensional strain sensing mechanosome drives adhesion-independent platelet activation at supraphysiological hemodynamic gradients(BMC, 2022-03-24)BACKGROUND: Supraphysiological hemodynamics are a recognized driver of platelet activation and thrombosis at high-grade stenosis and in blood contacting circulatory support devices. However, whether platelets mechano-sense hemodynamic parameters directly in free flow (in the absence of adhesion receptor engagement), the specific hemodynamic parameters at play, the precise timing of activation, and the signaling mechanism(s) involved remain poorly elucidated. RESULTS: Using a generalized Newtonian computational model in combination with microfluidic models of flow acceleration and quasi-homogenous extensional strain, we demonstrate that platelets directly mechano-sense acute changes in free-flow extensional strain independent of shear strain, platelet amplification loops, von Willebrand factor, and canonical adhesion receptor engagement. We define an extensional strain sensing "mechanosome" in platelets involving cooperative Ca2+ signaling driven by the mechanosensitive channel Piezo1 (as the primary strain sensor) and the fast ATP gated channel P2X1 (as the secondary signal amplifier). We demonstrate that type II PI3 kinase C2α activity (acting as a "clutch") couples extensional strain to the mechanosome. CONCLUSIONS: Our findings suggest that platelets are adapted to rapidly respond to supraphysiological extensional strain dynamics, rather than the peak magnitude of imposed wall shear stress. In the context of overall platelet activation and thrombosis, we posit that "extensional strain sensing" acts as a priming mechanism in response to threshold levels of extensional strain allowing platelets to form downstream adhesive interactions more rapidly under the limiting effects of supraphysiological hemodynamics.
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ItemRANS predictions of trailing-edge slot flows using heatflux closures developed with CFD-driven machine learning(GLOBAL POWER PROPULSION SOC, 2021)Accurate prediction of the wall temperature downstream of the trailing-edge slot is crucial to designing turbine blades that can withstand the harsh aerothermal environment in a modern gas turbine. Because of their computational efficiency, industry relies on low-fidelity tools like RANS for momentum and thermal field calculations, despite their known underprediction of wall temperature. In this paper, a novel framework using a branch of machine learning, geneexpression programming (GEP) [Zhao et al. 2020, J. Comp. Physics, 411:109413] is used to develop closures for the turbulent heat-flux to improve upon this underprediction. In the original use of GEP (“frozen” approach), the turbulent heat-flux from a high-fidelity database was used to evaluate the fitness of the candidate closures during the symbolic regression, however, the resulting closure had no information of the temperature field during the optimisation process. In this work, the regression process of the GEP instead incorporates RANS calculations to evaluate the fitness of the candidate closures. This allows the inclusion of the temperature field from RANS to advance the iterative regression, leading to a more integrated heat-flux closure development, and consequently more accurate and robust models. The GEP-based CFD-driven framework is demonstrated on a trailing edge slot configuration with three blowing ratios. Full a posteriori predictions from the new closures are compared to high-fidelity reference data and both conventional RANS closures and closures obtained from the “frozen” approach.
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ItemA framework to develop data-driven turbulence models for flows with organised unsteadiness(Elsevier, 2019-04-15)Turbulence modelling development has received a boost in recent years through assimilation of machine learning methods and increasing availability of high-fidelity datasets. This paper presents an approach that develops turbulence models for flows exhibiting organised unsteadiness. The novel framework consists of three parts. First, using triple decomposition, the high-fidelity data is split into organised motion and stochastic turbulence. A data-driven approach is then used to develop a closure only for the stochastic part of turbulence. Finally, unsteady calculations are conducted, which resolve the organised structures and model the unresolved turbulence using the developed bespoke turbulence closure. A case study of a wake with vortex shedding behind a normal flat plate, at a Reynolds number of 2,000, based on plate height and freestream velocity, is considered to demonstrate the method. The approach shows significant improvement in mean velocity and Reynold stress profiles compared with standard turbulence models.