Influence of shock wave propagation on dielectric barrier discharge plasma actuator performance

Interest in plasma actuators as active flow control devices is growing rapidly due to their lack of mechanical parts, light weight and high response frequency. Although the flow induced by these actuators has received much attention, the effect that the external flow has on the performance of the actuator itself must also be considered, especially the influence of unsteady high-speed flows which are fast becoming a norm in the operating flight envelopes. The primary objective of this study is to examine the characteristics of a dielectric barrier discharge (DBD) plasma actuator when exposed to an unsteady flow generated by a shock tube. This type of flow, which is often used in different studies, contains a range of flow regimes from sudden pressure and density changes to relatively uniform high-speed flow regions. A small circular shock tube is employed along with the schlieren photography technique to visualize the flow. The voltage and current traces of the plasma actuator are monitored throughout, and using the well-established shock tube theory the change in the actuator characteristics are related to the physical processes which occur inside the shock tube. The results show that not only is the shear layer outside of the shock tube affected by the plasma but the passage of the shock front and high-speed flow behind it also greatly influences the properties of the plasma

Spatial Characterization of Wetting in Porous Media Using Local Lattice-Boltzmann Simulations

Wettability is one of the critical parameters affecting multiphase flow in porous media. The wettability is determined by the affinity of fluids to the rock surface, which varies due to factors such as mineral heterogeneity, roughness, ageing, and pore-space geometry. It is well known that wettability varies spatially in natural rocks, and it is still generally considered a constant parameter in pore-scale simulation studies. The accuracy of pore-scale simulation of multiphase flow in porous media is undermined by such inadequate wettability models. The advent of in situ visualization techniques, e.g. X-ray imaging and microtomography, enables us to characterize the spatial distribution of wetting more accurately. There are several approaches for such characterization. Most include the construction of a meshed surface of the interface surfaces in a segmented X-ray image and are known to have significant errors arising from insufficient resolution and surface-smoothing algorithms. This work presents a novel approach for spatial determination of wetting properties using local lattice-Boltzmann simulations. The scheme is computationally efficient as the segmented X-ray image is divided into subdomains before conducting the lattice-Boltzmann simulations, enabling fast simulations. To test the proposed method, it was applied to two synthetic cases with known wettability and three datasets of imaged fluid distributions. The wettability map was obtained for all samples using local lattice-Boltzmann calculations on trapped ganglia and optimization on surface affinity parameters. The results were quantitatively compared with a previously developed geometrical contact angle determination method. The two synthetic cases were used to validate the results of the developed workflow, as well as to compare the wettability results with the geometrical analysis method. It is shown that the developed workflow accurately characterizes the wetting state in the synthetic porous media with an acceptable uncertainty and is better to capture extreme wetting conditions. For the three datasets of imaged fluid distributions, our results show that the obtained contact angle distributions are consistent with the geometrical method. However, the obtained contact angle distributions tend to have a narrower span and are considered more realistic compared to the geometrical method. Finally, our results show the potential of the proposed scheme to efficiently obtain wettability maps of porous media using X-ray images of multiphase fluid distributions. The developed workflow can help for more accurate characterization of the wettability map in the porous media using limited experimental data, and hence more accurate digital rock analysis of multiphase flow in porous media

Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features

One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201

Heat generation mechanisms of DBD plasma actuators

During the last twenty years DBD plasma actuators have been known by their ability for boundary layer flow control applications. However, their usefulness is not limited to this application field, they also present great utility for applications within the field of heat transfer, such as a way to improve the aerodynamic efficiency of film cooling of gas turbine blades, or de-icing and ice formation prevention. Nevertheless, there is a relative lack of information about DBD’s thermal characteristics and its heat generation mechanisms. This happens due to the extremely high electric fields in the plasma region and consequent impossibility of applying intrusive measurement techniques. Against this background, this work describes the physical mechanisms behind the generation of heat associated to the DBD plasma actuators operation. An experimental technique, based on calorimetric principles, was devised in order to quantify the heat energy generated during the plasma actuators operation. The influence of the dielectric thickness, as well as the dielectric material, were also evaluated during this work. The results were exposed and discussed with the purpose of a better understanding of the heat generation mechanisms behind the operation of DBD plasma actuators

MEE-DBD Plasma Actuator Effect on Aerodynamics of a NACA0015 Aerofoil: Separation and 3D Wake

© 2020, Springer Nature Switzerland AG. Dielectric barrier discharge (DBD) plasma actuators have received considerable attention by many researchers for various flow control applications. Having no moving parts, being light-weight, easily manufacturable, and their ability to respond almost instantly are amongst the advantages which has made them a popular flow control device especially for application on aircraft wings. The new configuration of DBDs which uses multiple encapsulated electrodes (MEE) has been shown to produce a superior and more desirable performance over the standard actuator design. The objective of the current study is to examine the effect of this new actuator configuration on the aerodynamic performance of an aerofoil under leading edge separation and wake interaction conditions. The plasma actuator is placed at the leading edge of a symmetric NACA 0015 aerofoil which corresponds to the location of the leading edge slat. The aerofoil is operated in a chord Reynolds number of 0.2×106. Surface pressure measurements along with the mean velocity profile of the wake using pitot measurements are used to determine the lift and drag coefficients, respectively. Particle image velocimetry (PIV) is also utilised to visualise and quantify the induced flow field. The results show improvement in aerodynamic performances of aerofoil under leading edge separation and also facing the wake region

Tracking trade transactions in water resource systems: A node- arc optimization formulation

[1] We formulate and apply a multicommodity network flow node-arc optimization model capable of tracking trade transactions in complex water resource systems. The model uses a simple node to node network connectivity matrix and does not require preprocessing of all possible flow paths in the network. We compare the proposed node-arc formulation with an existing arc-path (flow path) formulation and explain the advantages and difficulties of both approaches. We verify the proposed formulation model on a hypothetical water distribution network. Results indicate the arc-path model solves the problem with fewer constraints, but the proposed formulation allows using a simple network connectivity matrix which simplifies modeling large or complex networks. The proposed algorithm allows converting existing node-arc hydroeconomic models that broadly represent water trading to ones that also track individual supplier-receiver relationships (trade transactions)

Comparison of Network Intrusion Detection Performance Using Feature Representation

P. 463-475Intrusion detection is essential for the security of the components of any network. For that reason, several strategies can be used in Intrusion Detection Systems (IDS) to identify the increasing attempts to gain unauthorized access with malicious purposes including those base on machine learning. Anomaly detection has been applied successfully to numerous domains and might help to identify unknown attacks. However, there are existing issues such as high error rates or large dimensionality of data that make its deployment di cult in real-life scenarios. Representation learning allows to estimate new latent features of data in a low-dimensionality space. In this work, anomaly detection is performed using a previous feature learning stage in order to compare these methods for the detection of intrusions in network tra c. For that purpose, four di erent anomaly detection algorithms are applied to recent network datasets using two di erent feature learning methods such as principal component analysis and autoencoders. Several evaluation metrics such as accuracy, F1 score or ROC curves are used for comparing their performance. The experimental results show an improvement for two of the anomaly detection methods using autoencoder and no signi cant variations for the linear feature transformationS

Omega-3 Fatty Acids Reduce Adipose Tissue Macrophages in Human Subjects with Insulin Resistance

Fish oils (FOs) have anti-inflammatory effects and lower serum triglycerides. This study examined adipose and muscle inflammatory markers after treatment of humans with FOs and measured the effects of ω-3 fatty acids on adipocytes and macrophages in vitro. Insulin-resistant, nondiabetic subjects were treated with Omega-3-Acid Ethyl Esters (4 g/day) or placebo for 12 weeks. Plasma macrophage chemoattractant protein 1 (MCP-1) levels were reduced by FO, but the levels of other cytokines were unchanged. The adipose (but not muscle) of FO-treated subjects demonstrated a decrease in macrophages, a decrease in MCP-1, and an increase in capillaries, and subjects with the most macrophages demonstrated the greatest response to treatment. Adipose and muscle ω-3 fatty acid content increased after treatment; however, there was no change in insulin sensitivity or adiponectin. In vitro, M1-polarized macrophages expressed high levels of MCP-1. The addition of ω-3 fatty acids reduced MCP-1 expression with no effect on TNF-α. In addition, ω-3 fatty acids suppressed the upregulation of adipocyte MCP-1 that occurred when adipocytes were cocultured with macrophages. Thus, FO reduced adipose macrophages, increased capillaries, and reduced MCP-1 expression in insulin-resistant humans and in macrophages and adipocytes in vitro; however, there was no measureable effect on insulin sensitivity. Diabetes 62:1709–1717, 201