A5051919799- Fluid Dynamics and Turbulent Flows
- Heat Transfer Mechanisms
- Turbomachinery Performance and Optimization
- Fluid Dynamics and Vibration Analysis
- Combustion and flame dynamics
- Heat transfer and supercritical fluids
- Model Reduction and Neural Networks
- Wind and Air Flow Studies
- Aerodynamics and Fluid Dynamics Research
- Computational Fluid Dynamics and Aerodynamics
- Aerodynamics and Acoustics in Jet Flows
- Blood properties and coagulation
- Congenital Heart Disease Studies
- Platelet Disorders and Treatments
- Water resources management and optimization
- Heat Transfer and Optimization
- Hemoglobin structure and function
- Particle Dynamics in Fluid Flows
- Mechanical Circulatory Support Devices
- Evolutionary Algorithms and Applications
- Fluid Dynamics and Thin Films
- Meteorological Phenomena and Simulations
- Erythrocyte Function and Pathophysiology
- Water-Energy-Food Nexus Studies
- Gas Dynamics and Kinetic Theory
The University of Melbourne
2019-2024
Visa (United Kingdom)
2024
Delhi Technological University
2013
Improvements in turbulence modelling the recent years has seen an increasing prominence of various machine-learning algorithms. In this work, two different algorithms: tensor basis neural networks (TBNNs) and gene-expression programming (GEP), are combined to extract interpretable Reynolds stress closures. Representations high-fidelity stress, obtained from deep-learning, used learn symbolic expressions for This is contrast previously developed approaches using either or regression...
Abstract Background Supraphysiological hemodynamics are a recognized driver of platelet activation and thrombosis at high-grade stenosis in blood contacting circulatory support devices. However, whether platelets mechano-sense hemodynamic parameters directly free flow (in the absence adhesion receptor engagement), specific play, precise timing activation, signaling mechanism(s) involved remain poorly elucidated. Results Using generalized Newtonian computational model combination with...
View Video Presentation: https://doi.org/10.2514/6.2022-0696.vid This paper describes a collaborative experimental and computational study of smooth wall boundary layers in systematic family favorable adverse pressure gradients. The objective is to advance turbulence modeling these flows, particular the effects gradients that can be classified as non-equilibrium. collaboration component larger NATO AVT-349 Research Task Group. Experiments under this effort are conducted at Virginia Tech...
Accurate prediction of the wall temperature downstream trailing-edge slot is crucial to designing turbine blades that can withstand harsh aerothermal environment in a modern gas turbine. Because their computational efficiency, industry relies on low-fidelity tools like RANS for momentum and thermal field calculations, despite known underprediction temperature. In this paper, novel framework using branch machine learning, geneexpression programming (GEP) [Zhao et al. 2020, J. Comp. Physics,...
The circulation in the total cavopulmonary connection (TCPC) is a low-energy system which operation and efficiency are subjected to multiple factors. Some retrospective studies report that abnormal narrowing of vessels system, i.e. stenosis, one most dangerous geometric factors can result heart failure. In present study, effect varying extracardiac conduit (ECC) stenosis on hemodynamics surrogate TCPC model investigated using high-fidelity numerical simulations. was quantified according...
Abstract The unsteady flow prediction for turbomachinery applications relies heavily on RANS (URANS). For flows that exhibit vortex shedding, such as the wall-jet/wake considered in this study, URANS is unable to predict correct momentum mixing with sufficient accuracy. We suggest a novel framework improve prediction, whereby deterministic scales associated shedding are resolved while stochastic of pure turbulence modelled. first separates from length and then develops bespoke closure using...
Abstract The trailing edge slot is a canonical representation of the pressure-side bleed flow encountered in high pressure turbines. Predicting and temperature downstream exit remains challenging for RANS URANS, with both significantly overpredicting adiabatic wall-effectiveness. This over-prediction attributable to incorrect mixing prediction cases where vortex shedding present. In case modelling error rooted not properly accounting scales while URANS closures account twice, once by...
Natural convection is a commonly occurring heat-transfer problem in many industrial flows and its prediction with conventional large eddy simulations (LES) at higher Rayleigh numbers using progressively coarser grids leads to increasingly inaccurate estimates of the Nusselt number (Nu). Thus, improve Nu predictions, we utilise Gene Expression Programming (GEP) develop sub-grid scale (SGS) stress heat-flux models for LES. The models’ development performed reference direct numerical simulation...
Natural convection heat transfer at very high Rayleigh numbers (\textit{Ra}) is a commonly occurring phenomenon in many industry applications. Practically, Large Eddy Simulations (LES) are often chosen as an economical method for the numerical prediction of these flows. However, developing accurate LES models that generalize well to complex geometries poses challenge, particularly data-driven methods. Thus, current study, machine-learnt closure with embedded geometry independence proposed,...
The trailing edge slot is a canonical representation of the pressure-side bleed flow encountered in high pressure turbines.Predicting and temperature downstream exit remains challenging for RANS URANS, with both significantly overpredicting adiabatic wall-effectiveness.This overprediction attributable to incorrect mixing prediction cases where vortex shedding present.In case modelling error rooted not properly accounting scales while URANS closures account twice, once by resolving twice...