Fundamental analysis of liquid breakup mechanism in a rotary atomizer with square discharge orifice

[EN] An experimental investigation of breakup mechanism in a rotary atomizer with square shape discharge orifice at ambient condition has been performed. The effects of a high aspect ratio noncircular discharge channels, particularly a square shape discharge channel, are considered. The motivation of this study is the use of this type of orifice in some small gas turbine engines as well as non-existing observation in literature concerning about high aspect ratio of discharge channel. Visualization experiments are conducted by high speed shadowgraph imaging technique with pulsed light illumination for the first time. The effects of rotational speed and volume flow rate are studied on the breakup structure. The visualizations indicates that the liquid film formed along the channel is pushed to one side of it due to Coriolis force which is dominant in this type of atomizer. Accordingly a crescent shaped liquid film is formed at the square channel exit covering two corners of the square, resulting the combination of Coriolis induced stream mode and surface tension induced stream mode breakup. Observations of the breakup process for different volume flow rates and rotational speeds indicate that the breakup of liquid film stream is dependent on injection conditions and the corresponding cross flow velocity created by atomizer rotation. The breakup regime map is provided as a function of weber number and momentum flux ratio. Four distinct regimes are identified: Rayleigh breakup, bag breakup, multimode breakup, and shear breakup. The present results leads to understanding atomization performance and creating some idea to improved spray quality in this type of atomizer.Ghorbanhoseini, M.; Rezayat, S.; Farshchi, M. (2017). Fundamental analysis of liquid breakup mechanism in a rotary atomizer with square discharge orifice. En Ilass Europe. 28th european conference on Liquid Atomization and Spray Systems. Editorial Universitat Politècnica de València. 496-503. https://doi.org/10.4995/ILASS2017.2017.5640OCS49650

Agent-based distributed performance measurement system for ITSP projects

Measuring the development of an enterprise software project progress and performance is crucial to assure a project follow its path. This attention stems in parts from the frequent reports of high profile cases of mismanaged in IT projects particularly in enterprise level such as information technology strategic planning (ITSP) projects. Many project performance measurement models and frameworks have been proposed to address the problem, however, they are usually hard to manage and inefficient in practice due to the complexity, distribution and dynamism of these types of projects. A large and growing body of literature has shown the advantages of employing the agent technology in distributed, dynamic, and complex environments. Therefore, in this study the advantages of the agent technology will be applied to improve the progress and measurement process of the software project performance measurement approaches. In this paper, a multi-agent system architectural model with the focus in implementation phase of the ITSP projects is proposed to promote and facilitate the process of project performance measurement. Furthermore the prototype of the proposed solution is explained and the evaluation approach is discussed

Technical Report: Anomaly Detection for a Critical Industrial System using Context, Logs and Metrics

Recent advances in contextual anomaly detection attempt to combine resource metrics and event logs to un- cover unexpected system behaviors and malfunctions at run- time. These techniques are highly relevant for critical software systems, where monitoring is often mandated by international standards and guidelines. In this technical report, we analyze the effectiveness of a metrics-logs contextual anomaly detection technique in a middleware for Air Traffic Control systems. Our study addresses the challenges of applying such techniques to a new case study with a dense volume of logs, and finer monitoring sampling rate. We propose an automated abstraction approach to infer system activities from dense logs and use regression analysis to infer the anomaly detector. We observed that the detection accuracy is impacted by abrupt changes in resource metrics or when anomalies are asymptomatic in both resource metrics and event logs. Guided by our experimental results, we propose and evaluate several actionable improvements, which include a change detection algorithm and the use of time windows on contextual anomaly detection. This technical report accompanies the paper “Contextual Anomaly Detection for a Critical Industrial System based on Logs and Metrics” [1] and provides further details on the analysis method, case study and experimental results

System dynamics simulation model to assess impacts of personnel factors on delayed software projects

According to the latest survey‘s reports, the software project‘s failure is considerably high despite the advancement in the field of software project management. Many projects were being announced to be completed beyond their planned schedule. Delivery of a software project beyond its schedule cannot be tolerated. The delay may result in the loss of a market opportunity and can be a cause of failure to a dependent project . Schedule slippage could even incur a much higher cost than the cost of the project itself. Therefore, software project managers may take any possible arrangement to ensure that the project completed on time, such as adding new manpower to the project. Adding new manpower to an ongoing delayed software project may cause negative impacts to the team productivity due to assimilation time, training overhead and communication overhead. Consequently, project managers have difficulties to make the decision whether to add new members to his team or not. Therefore, this research attempts to investigate how software project managers can minimize the negative influence of adding new manpower to delayed software projects. More specifically, this research aims to examine whether a significant schedule improvement can be achieved with proper consideration of the new manpower capabilities, skills and experience. A system dynamics approach has been employed for in-depth investigation of the issue. Accordingly, a System Dynamics Simulation Model is proposed to simulate the dynamic behaviour of the project progress when new members are added to the project. Some principal attributes of the model such as assimilation time, communication overhead and training overhead have been adopted from the previously developed system dynamics models. The COCOMO II personnel factors productivity multipliers are also employed to represent various personnel factors in the proposed model. The proposed model was verified and validated by experiments with two cases: a literature case and an industry case. The results of the experiments indicate that significant schedule improvement of a late project can be achieved if people with certain levels of capabilities and experience are added to the project. Furthermore, the findings of this research demonstrates that software project managers can take the advantages of employing system dynamics approach for detailed trade-off analysis of adding new manpower to a project

Reassessing Brooks' law through consideration of manpower abilities

When software projects falls behind schedule, managers make any possible reactions to avoid any schedule slippage, mostly by adding new people to their projects. Adding more people to a late software project may cause negative impacts on the progress of the project as the result of assimilation time, training overhead and communication overhead. Consequently, a project manager faces difficulties to make the decision whether to add new members to his team or not. Therefore, in this research we attempted to address the problem by understanding how software project managers can minimize the negative impacts of adding new people to delayed software projects. This research aims to explore the effects of adding manpower to a late software project through personnel factors trade-off analysis. Particularly, this study has the intention to examine whether a significant schedule improvement can be achieved with proper consideration of the new manpower capabilities, skills and experience. For fulfillment of this research, we built a system dynamics model to simulate the dynamic behavior of the project progress when new members added with different ranges of personnel capabilities. In addition, an example case study has been run and related simulation results are compared with previous models. The result of this study shows that significant schedule improvement of a late project can be achieved if people with certain level of capabilities are added to a project

Experience report: anomaly detection of cloud application operations using log and cloud metric correlation analysis

Failure of application operations is one of the main causes of system-wide outages in cloud environments. This particularly applies to DevOps operations, such as backup, redeployment, upgrade, customized scaling, and migration that are exposed to frequent interference from other concurrent operations, configuration changes, and resources failure. However, current practices fail to provide a reliable assurance of correct execution of these kinds of operations. In this paper, we present an approach to address this problem that adopts a regression-based analysis technique to find the correlation between an operation's activity logs and the operation activity's effect on cloud resources. The correlation model is then used to derive assertion specifications, which can be used for runtime verification of running operations and their impact on resources. We evaluated our proposed approach on Amazon EC2 with 22 rounds of rolling upgrade operations while other types of operations were running and random faults were injected. Our experiment shows that our approach successfully managed to raise alarms for 115 random injected faults, with a precision of 92.3%