N.A.K. Doan
47 records found
1
This paper investigates water injection effects in a simplified Ansaldo GT36 reheat system under realistic conditions of 20 atm using large eddy simulation (LES) coupled with thickened flame modeling and adaptive mesh refinement. The water injection conditions are optimized by pe
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Phase-resolved volumetric velocity measurements of a pulsed jet are conducted by means of three-dimensional particle tracking velocimetry (PTV). The resulting scattered and relatively sparse data are densely reconstructed by adopting physics-informed neural networks (PINNs), here
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Permeable materials are a promising trailing edge noise reduction technique. The noise reduction is a result of the unsteady interaction between the two communicating boundary layers, in a process referred to as the pressure release mechanism. However, in practice the aeroacousti
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A design can only be as good as its mathematical representation. In engineering design optimization, the chosen method of parameterization can have significant impact on the outcomes. This paper introduces a novel methodology for airfoil design parameterization utilizing variatio
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This paper presents an investigation of the effects of water injection within a simplified version of the Ansaldo GT36 reheat system. The investigation is carried out under realistic operating conditions of 20 atm and using large eddy simulation (LES) coupled with the thickened f
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The implementation of the Particle Swarm Optimization (PSO) algorithm is investigated to optimize the active attenuation of Tollmien–Schlichting (TS) waves developing in a two-dimensional zero pressure gradient boundary layer. This is done numerically, where the PSO algorithm opt
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In this work, we propose a data-driven framework to identify precursors of extreme events in turbulent reacting flows. Specifically, we tackle the problem of flashback prediction in a lean hydrogen reheat combustor. Our framework is composed of two parts. The first consists in th
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Phase-resolved volumetric velocity measurements of a pulsed jet are conducted by means of three-dimensional particle tracking velocimetry (PTV). The resulting scattered and relatively sparse data are densely reconstructed by adopting physics-informed neural networks (PINNs), here
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This work focuses on the suppression of Tollmien-Schlichting (TS) waves in a two-dimensional laminar boundary layer using optimized unsteady suction and blowing jets as an Active Flow Control (AFC) method. The suppression of TS waves via this AFC system is enabled through two art
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This paper introduces an approach for parameterizing airfoil geometries using a Variational Autoencoder (VAE) with a focus on achieving a low-dimensional and interpretable model. The primary focus is to facilitate efficient use in design optimization environments by capturing ess
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A lean premixed ethylene-air flame in a backstep configuration is simulated on multiple grids using both direct numerical simulations (DNS) with reduced order kinetic mechanism and large eddy simulations (LES) with flamelet-based thermochemistry. The configuration includes prehea
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Modeling complex dynamical systems with only partial knowledge of their physical mechanisms is a crucial problem across all scientific and engineering disciplines. Purely data-driven approaches, which only make use of an artificial neural network and data, often fail to accuratel
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The dynamics of turbulent flows is chaotic and difficult to predict. This makes the design of accurate reduced-order models challenging. The overarching objective of this paper is to propose a nonlinear decomposition of the turbulent state to predict the flow based on a reduced-o
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Turbulence is characterised by chaotic dynamics and a high-dimensional state space, which make this phenomenon challenging to predict. However, turbulent flows are often characterised by coherent spatiotemporal structures, such as vortices or large-scale modes, which can help obt
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Abrupt and rapid high-amplitude changes in a dynamical system’s states known as extreme events appear in many processes occurring in nature, such as drastic climate patterns, rogue waves, or avalanches. These events often entail catastrophic effects, therefore their description a
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Acoustic measurements, obtained by microphones positioned at strategic places, are of great utility for the monitoring of a given acoustic system and for its protection in case large pressure fluctuations are measured. Such strategies are reliable as long as the microphones are p
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Pool fires are canonical representations of many accidental fires which can exhibit an unstable unsteady behavior, known as puffing, which involves a strong coupling between the temperature and velocity fields. Despite their practical relevance to fire research, their experimenta
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SGS Reaction rate modelling for MILD combustion based on machine-learning combustion mode classification
Development and a priori study
A neural network (NN) aided model is proposed for the filtered reaction rate in moderate or intense low-oxygen dilution (MILD) combustion. The framework of the present model is based on the partially stirred reactor (PaSR) approach, and the fraction of the reactive structure appe
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Multi-scale analysis of turbulence-flame interaction is performed using experimental data sets from three methane- and propane-fired premixed, turbulent V-flames, at an approach flow turbulent Reynolds number of 450 and a ratio of r.m.s. fluctuating velocity from the mean to lami
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Performing measurements in reacting flows is a challenging task due to the complexity of measuring all quantities of interest simultaneously or limitations in the optical access. To compensate for this, recent advances in deep learning have shown a strong potential in augmenting
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