A unified approach for numerical simulation of viscous compressible and incompressible flows over adiabatic and isothermal walls

A new formulation (including the choice of variables, their non-dimensionalization, and the form of the artificial viscosity) is proposed for the numerical solution of the full Navier-Stokes equations for compressible and incompressible flows with heat transfer. With the present approach, the same code can be used for constant as well as variable density flows. The changes of the density due to pressure and temperature variations are identified and it is shown that the low Mach number approximation is a special case. At zero Mach number, the density changes due to the temperature variation are accounted for, mainly through a body force term in the momentum equation. It is also shown that the Boussinesq approximation of the buoyancy effects in an incompressible flow is a special case. To demonstrate the new capability, three examples are tested. Flows in driven cavities with adiabatic and isothermal walls are simulated with the same code as well as incompressible and supersonic flows over a wall with and without a groove. Finally, viscous flow simulations of an oblique shock reflection from a flat plate are shown to be in good agreement with the solutions available in literature

Interleaving Gains for Receive Diversity Schemes of Distributed Turbo Codes in Wireless Half–Duplex Relay Channels

This paper proposes the interleaving gain in two different distributed turbo-coding schemes: Distributed Turbo Codes (DTC) and Distributed Multiple Turbo Codes (DMTC) for half-duplex relay system as an extension of our previous work on turbo coding interleaver design for direct communication channel. For these schemes with half-duplex constraint, the source node transmits its information with the parity bit sequence(s) to both the relay and the destination nodes during the first phase. The relay received the data from the source and process it by using decode and forward protocol. For the second transmission period, the decoded systematic data at relay is interleaved and re-encoded by a Recursive Systematic Convolutional (RSC) encoder and forwarded to the destination. At destination node, the signals received from the source and relay are processed by using turbo log-MAP iterative decoding for retrieving the original information bits. We demonstrate via simulations that the interleaving gain has a large effect with DTC scheme when we use only one RSC encoder at both the source and relay with best performance when using Modified Matched S-Random (MMSR) interleaver. Furthermore, by designing a Chaotic Pseudo Random Interleaver (CPRI) as an outer interleaver at the source node instead of classical interleavers, our scheme can add more secure channel conditions

Semiclassical Hartree-Fock theory of a rotating Bose-Einstein condensation

In this paper, we investigate the thermodynamic behavior of a rotating Bose-Einstein condensation with non-zero interatomic interactions theoretically. The analysis relies on a semiclassical Hartree-Fock approximation where an integral is performed over the phase space and function of the grand canonical ensemble is derived. Subsequently, we use this result to derive several thermodynamic quantities including the condensate fraction, critical temperature, entropy and heat capacity. Thereby, we investigate the effect of the rotation rate and interactions parameter on the thermodynamic behavior. The role of finite size is discussed. Our approach can be extended to consider the rotating condensate in optical potential

The role of subsidiaries in Global Value Chains (GVCs): an institutional voids perspective on LVC upgrading and integration

We explore the process through which MNE subsidiaries engage and retain a critical mass of small suppliers in Global Value Chains (GVCs) while addressing institutional voids in emerging markets. Using evidence from an interpretive inductive longitudinal case study in agribusiness, we draw on the GVC and institutional voids literatures to: (1) extend the GVC literature by offering a subsidiary-focused view of GVCs; and (2) demonstrate the dynamic process of void engagement through complementary institutional bridging activities. Our temporal sequencing of subsidiary institutional agency in response to different modalities of voids demonstrates a constellation of bridging activities that results from a dynamic interplay between voids and practice

Effect of Building Configuration on Seismic Response Parameters

To contribute to the available information on the inelastic performance of irregular structures, the investigation of four building characteristics on it seismic response was initiated. These characteristics are column height, beam-to-column capacity, stiffness distribution in elevation and set-backs and non-symmetric elevation configuration. The parametric study presented in the paper is intended to be more indicative than comprehensive, since simplifications in the modeling of structures were necessary

Potato Classification Using Deep Learning

Abstract: Potatoes are edible tubers, available worldwide and all year long. They are relatively cheap to grow, rich in nutrients, and they can make a delicious treat. The humble potato has fallen in popularity in recent years, due to the interest in low-carb foods. However, the fiber, vitamins, minerals, and phytochemicals it provides can help ward off disease and benefit human health. They are an important staple food in many countries around the world. There are an estimated 200 varieties of potatoes, which can be classified into a number of categories based on the cooked texture and ingredient functionality. Using a public dataset of 2400 images of potatoes, we trained a deep convolutional neural network to identify 4 types (Red, Red Washed, Sweet, and White).The trained model achieved an accuracy of 99.5% of test set, demonstrating the feasibility of this approach

On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow

Abundant data is the key to successful machine learning. However, supervised learning requires annotated data that are often hard to obtain. In a classification task with limited resources, Active Learning (AL) promises to guide annotators to examples that bring the most value for a classifier. AL can be successfully combined with self-training, i.e., extending a training set with the unlabelled examples for which a classifier is the most certain. We report our experiences on using AL in a systematic manner to train an SVM classifier for Stack Overflow posts discussing performance of software components. We show that the training examples deemed as the most valuable to the classifier are also the most difficult for humans to annotate. Despite carefully evolved annotation criteria, we report low inter-rater agreement, but we also propose mitigation strategies. Finally, based on one annotator's work, we show that self-training can improve the classification accuracy. We conclude the paper by discussing implication for future text miners aspiring to use AL and self-training.Comment: Preprint of paper accepted for the Proc. of the 21st International Conference on Evaluation and Assessment in Software Engineering, 201

Optimum Remedial Operation of Permanent Magnet Synchronous Motor

In critical systems, the reliability of the drive is very important. The faults are unwanted. The faults may be lead to loss of the human life and capital. This paper is addressed this problem and suggested two models to solve it. The first model doesn’t contain any special tools to improve the torque ripple and THD. The second model contains 2PI current controllers to improvement the performance at fault and remedial operation. One is for the torque and the other is for the flux. The first PI controller is feeding from the torque error between the reference and estimated torques to get new q-axis current component representing modifier current arises from uncertain things inside the machine and drive system such as temperature and parameters variations. This current will add to reference q-axis current to get robust new q-axis current to satisfy the drive requirement and solve the torque problem (ripple torque). With robust current, the total harmonic distortion is a decrease but doesn’t reach the best value so the other PI controller is used to adjust the THD. In this PI controller, the d-axis flux is compared to rotor permanent magnet flux to solve this problem arises from non-sinusoidal of the magnetic flux. The output of the PI controller is introduced to the reference d-axis current. The new d-axis current will reach the best value of THD. The simulation of the second controller is compared to the simulation of first controller to show if the second controller strong or weak. Matlab simulink is used to simulate the drive system.DOI:http://dx.doi.org/10.11591/ijece.v2i5.72

Assessment methods determining the higher education students’ academic success

It is well known that academic success of undergraduate students depends on a variety of factors; several of them are external to their higher education institution. However, it is crucial to reflect on the impact of the factors controlled by the higher education institution (e.g., faculty or college) that can influence their success and achievement, including the assessment methods. To this end, this study analyzes the extent to which the assessment methods have a substantial impact on approval rates of students. In doing so, 797 averages of course grades from a Portuguese higher education institution were collected in different academic years between 2013/2014 and 2017/2018. The significance effect of the course field, laboratories, projects, mini-tests, group work, individual work, frequencies, exercises, and presentations on the final averages of the courses was evaluated based on the modeling of structural equations. The results showed that the use of laboratories, presentations, individual and group-based works/tasks are the most explanatory elements (25%) for positive averages. The remainder is justified by other factors associated with students, such as socio-economic, previous education, and motivational factors that explain academic success. In future work, the proposed model, with different teaching strategies, could be studied and evaluated within different educational contexts.info:eu-repo/semantics/acceptedVersio