1,721,221 research outputs found
Convolutional Autoencoder for the Spatiotemporal Latent Representation of Turbulence
No full textTurbulence 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 obtain a latent description of turbulent flows. However, current approaches are often limited by either the need to use some form of thresholding on quantities defining the isosurfaces to which the flow structures are associated or the linearity of traditional modal flow decomposition approaches, such as those based on proper orthogonal decomposition. This problem is exacerbated in flows that exhibit extreme events, which are rare and sudden changes in a turbulent state. The goal of this paper is to obtain an efficient and accurate reduced-order latent representation of a turbulent flow that exhibits extreme events. Specifically, we employ a three-dimensional multiscale convolutional autoencoder (CAE) to obtain such latent representation. We apply it to a three-dimensional turbulent flow. We show that the Multiscale CAE is efficient, requiring less than 10% degrees of freedom than proper orthogonal decomposition for compressing the data and is able to accurately reconstruct flow states related to extreme events. The proposed deep learning architecture opens opportunities for nonlinear reduced-order modeling of turbulent flows from data.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Aerodynamic
Understand customer satisfaction through customer online reviews of online food delivery apps
No full textOnline food delivery (OFD) industry has recently gained popularity, especially after the onset of the COVID-19 pandemic. Like other service businesses, customer satisfaction plays a crucial role in the success of OFD companies. Online reviews, through which customers nowadays express their opinions about products or services, have recently been used to examine customer satisfaction in both academia and business world. However, there is limited research on this topic in the context of OFD. This study aims to understand OFD customer satisfaction via online reviews by examining more than 24000 online reviews from Apple App Store and Google Play Store of two OFD services that operate mainly in Europe.
In this study, various natural language processing methods and Latent Dirichlet Allocation topic modeling method were used to process data and operationalize variables. Drawing on the definition of customer satisfaction in the extant literature, a customer satisfaction conceptual model, that investigates the effects of review subjectivity, and negative emotions (sadness, disgust, and anger) on review ratings, was developed and tested by multiple linear regression. Those effects on review ratings are also examined across review topics.
The results reveal that negative emotions (sadness, disgust, and anger) are negatively associated with review ratings. Reviews written in a more subjective manner have higher numeric ratings; however, negative emotions moderate this effect. In addition, the degrees of these effects vary across different review topics, unveiling the role of review topics in impacting review ratings. These findings help enhance our understanding of the role of subjectivity, negative emotions, and review topics in online reviews with respect to OFD customer satisfaction management. However, it still requires caution when generalizing the findings to other contexts.
This study expands on earlier research efforts regarding online reviews and customer satisfaction by introducing a customer satisfaction conceptual model that uses both subjectivity and emotional aspects of the textual reviews to explain review ratings. Moreover, this research offers vital insights and suggests beneficial practical recommendations for OFD businesses to improve their services and ensure customer satisfaction. This study also contributes to the groundwork for future research that establishes a more sophisticated customer satisfaction framework, examines OFD aspects across regions and cultures, and performs competitor benchmarking
Physics-Informed Data-Driven Prediction of Turbulent Reacting Flows with Lyapunov Analysis and Sequential Data Assimilation
Full text linkHigh-fidelity simulations of turbulent reacting flows enable scientific understanding of the physics and engineering design of practical systems. Whereas Direct Numerical Simulation (DNS) is the most suitable numerical tool to understand the physics, under-resolved and large-eddy simulations offer a good compromise between accuracy and computational effort in the prediction of engineering flows. This compromise speeds up the computations but reduces the space-and-time accuracy of the prediction. The objective of this chapter is to (i) evaluate the predictability horizon of turbulent simulations with chaos theory, and (ii) enable the space-and-time-accurate prediction of rare and transient events using a Bayesian statistical learning approach based on data assimilation. The methods are applied to DNS of Moderate or Intense Low-oxygen Dilution (MILD) combustion. The predictability provides an estimate of the time horizon within which the occurrence of ignition kernels and deflagrative modes, which are considered here as rare and transient events, can be accurately predicted. The accurate detection of ignition kernels and their evolution towards deflagrative structures are well captured on a coarse (under-resolved) grid when data is assimilated from a costly refined DNS. Physically, such an accurate prediction is important to understand the stabilization mechanism of MILD combustion. These techniques enable the space-and-time-accurate prediction of rare and transient events in turbulent flows by combining under-resolved simulations and experimental data, for example, from engine sensors. This opens up new possibilities for on-the-fly calibration of reduced-order models for turbulent reacting flows
Master of Marketing [in brief]
No full textCourse lecturer Doan Nguyen introduces the Master of Marketing program at the AGSE
Master of Marketing [in brief – social media version]
No full textCourse lecturer Doan Nguyen introduces the Master of Marketing program at the AGSE
Characterization of Communicating Turbulent Grazing Flows Through a Resolved Porous Medium
No full textPorous media are a promising technology to reduce turbulent boundary layer trailing edge noise. However, the fact that the porous material is grazed by turbulent flow on both sides makes its characterization not trivial. This paper describes the modifications resulting from the interaction between the grazing flows through the porous medium, defined as communication. To this end, lattice-Boltzmann simulations of two communicating turbulent channel flows separated by a fully resolved porous medium are carried out. The porous medium is realized as a 75% porous triply periodic minimal surface of type Schwarz’ P. Results are compared against the case with porous medium backed by a solid wall and the smooth wall channel flow. When communication between the two channel flows is allowed, spanwise coherent structures appear that are assimilated to a
shear instability at a non-dimensional frequency of Stt = 0.02. Instantaneous flow through the porous medium is observed and is driven by a time-dependent pressure differential between the channels (with a zero mean and 7.8 Pa standard deviation). This leads to a decrease in energy in turbulent scales smaller than 2.5δ and for bulk scaled frequencies greater than Stb = 0.41. These flow modifications are not observed in the non-communicating case, with the wall preventing flow through, where the topology of the fluctuating statistics is similar to the smooth wall case. Finally, the drag is found to increase by over 200%
with respect to the non-communicating case and 650% with respect to a smooth turbulent channel flow. The drag increase is found to be driven by the velocity fluctuations impinging on the porous topology. The communication does not fol-
low the asymptotic drag relation for the same equivalent roughness, thus entering a different drag regime
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Aerodynamic Characterisation of Communicating Turbulent Boundary Layers Through a Porous Medium Subjected to a Pressure Differential
Full text linkPermeable 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 aeroacoustic performance of permeable trailing edges degrades under lifting conditions, i.e. with a pressure and velocity differential. This study aims at investigating such flow physics using the Lattice Boltzmann Method through 3DS PowerFLOW. A numerical setup was created to explore the impact of velocity and pressure differentials between two communicating boundary layers and relate them to the aeroacoustic performance of porous media. The proposed numerical setup consists of two vertically stacked temporally developing channel flows separated by a porous medium (6δ x (4δ+t) x 2δ), where δ and t are the half-channel height and the porous medium thickness respectively. The two channel flows communicate through fully resolved porous media, here, 75\% porous triply periodic minimal surfaces. A large drag increase is observed for all geometries. An increase in anti-correlation between the pressure fluctuations between the channels is found to be related to a drag increase. It was concluded that the spanwise coherent turbulent structures drive the increase in drag. These structures are also affected by the geometry of the porous medium at the surface of the grazing flows. The presence of large coherent turbulent structures leads to a shift in turbulent energy scales. This is related to the modification of the wall pressure spectrum, where it was observed that less energy is present at low frequencies, whereas a peak was observed at a higher frequency. The crossover frequency is between 150Hz and 600Hz
Master of Marketing
No full textDr Doan Nguyen explains how the AGSE Master of Marketing program utilises industry partners in the co-creation of course content
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