Petrol engine workflow model for researching unconventional engines

A V Khimchenko; N I Mishchenko; T N Kolesnikova; V L Suprun; Yu V Yurchenko

doi:10.1088/1755-1315/659/1/012074

Petrol engine workflow model for researching unconventional engines

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/659/1/012074 ◽

2021 ◽

Vol 659 (1) ◽

pp. 012074

Author(s):

A V Khimchenko ◽

N I Mishchenko ◽

T N Kolesnikova ◽

V L Suprun ◽

Yu V Yurchenko

Keyword(s):

Petrol Engine ◽

Workflow Model

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Small craft. Ventilation of petrol engine and/or petrol tank compartments

10.3403/30352013 ◽

2018 ◽

Keyword(s):

Petrol Engine ◽

Small Craft

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Digital Twins Collaboration for Automatic Erratic Operational Data Detection in Industry 4.0

Applied Sciences ◽

10.3390/app11073186 ◽

2021 ◽

Vol 11 (7) ◽

pp. 3186

Author(s):

Radhya Sahal ◽

Saeed H. Alsamhi ◽

John G. Breslin ◽

Kenneth N. Brown ◽

Muhammad Intizar Ali

Keyword(s):

Industry 4.0 ◽

Manufacturing Systems ◽

Interaction Mechanism ◽

Semantic Knowledge ◽

Smart Manufacturing ◽

Data Detection ◽

Workflow Model ◽

Digital Twins ◽

Potential Failure ◽

Operational Data

Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the interactions between the DTs to pave the way for the cyber-physical integration of smart manufacturing. This paper presents a conceptual framework for digital twins collaboration to provide an auto-detection of erratic operational data by utilizing operational data intelligence in the manufacturing systems. The proposed framework provide an interaction mechanism to understand the DT status, interact with other DTs, learn from each other DTs, and share common semantic knowledge. In addition, it can detect the anomalies and understand the overall picture and conditions of the operational environments. Furthermore, the proposed framework is described in the workflow model, which breaks down into four phases: information extraction, change detection, synchronization, and notification. A use case of Energy 4.0 fault diagnosis for wind turbines is described to present the use of the proposed framework and DTs collaboration to identify and diagnose the potential failure, e.g., malfunctioning nodes within the energy industry.

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Challenges and Opportunities in Collaborative Vulnerability Research Workflows

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641094 ◽

2020 ◽

Vol 64 (1) ◽

pp. 420-424

Author(s):

Ryan Mullins ◽

Deirdre Kelliher ◽

Ben Nargi ◽

Mike Keeney ◽

Nathan Schurr

Keyword(s):

Human Performance ◽

Collaborative Work ◽

Research Process ◽

Workflow Model ◽

Human Vulnerability ◽

Research Teams ◽

Reasoning System ◽

Challenges And Opportunities ◽

New Research ◽

Future Work

Recently, cyber reasoning systems demonstrated near-human performance characteristics when they autonomously identified, proved, and mitigated vulnerabilities in software during a competitive event. New research seeks to augment human vulnerability research teams with cyber reasoning system teammates in collaborative work environments. However, the literature lacks a concrete understanding of vulnerability research workflows and practices, limiting designers’, engineers’, and researchers’ ability to successfully integrate these artificially intelligent entities into teams. This paper contributes a general workflow model of the vulnerability research process, and identifies specific collaboration challenges and opportunities anchored in this model. Contributions were derived from a qualitative field study of work habits, behaviors, and practices of human vulnerability research teams. These contributions will inform future work in the vulnerability research domain by establishing an empirically-driven workflow model that can be adapted to specific organizational and functional constraints placed on individual and teams.

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