Large-Eddy Simulation and experimental study of cycle-to-cycle variations of stable and unstable operating points in a spark ignition engine

This article presents a comparison between experiments and Large-Eddy Simulation (LES) of a spark ignition engine on two operating points: a stable one characterized by low cycle-to-cycle variations (CCV) and an unstable one with high CCV. In order to match the experimental cycle sample, 75 full cycles (with combustion) are computed by LES. LES results are compared with experiments by means of pressure signals in the intake and exhaust ducts, in-cylinder pressure, chemiluminescence and OH Planar Laser Induced Fluorescence (PLIF). Results show that LES is able to: (1) reproduce the flame behavior in both cases (low and high CCV) in terms of position, shape and timing; (2) distinguish a stable point from an unstable one; (3) predict quantitatively the CCV levels of the two fired operating points. For the unstable case, part of the observed CCV is due to incomplete combustion. The results are then used to analyze the incomplete combustion phenomenon which occurs for some cycles of the unstable point and propose modification of the spark location to control CCV

Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study

Comparative Analysis of Open Source Frameworks for Machine Learning with Use Case in Single-Threaded and Multi-Threaded Modes

The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared. Their comparative analysis was performed and conclusions were made as to the advantages and disadvantages of these platforms. The performance tests for the de facto standard MNIST data set were carried out on H2O framework for deep learning algorithms designed for CPU and GPU platforms for single-threaded and multithreaded modes of operation.Comment: 4 pages, 6 figures, 4 tables; XIIth International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT 2017), Lviv, Ukrain