demonstrated that pressure drop increases substantially if dry slug flow occurs or if microchannels with significant surface roughness are employed. Those influences were not accounted for in the models presented.
KeywordsMultiphase flow • Three-phase flow • Liquidliquid-gas • Slug flow • Microchannel • Pressure drop • Surface roughness List of symbols A Area (m 2 ) c Constant in Bretherton's pressure drop equation, c = 9.04 in this work (-) Ca Capillary number (-) Ca b Capillary number based on bubble velocity at the tube outlet and gas-liquid surface tension (-) Ca d Capillary number based on bubble velocity at the tube outlet and liquid-liquid surface tension (-) D Diameter (m) d c Diameter of the test tube (m) f Slug frequency (1/s) h Film thickness (m) L Length (m) ΔP Pressure drop (Pa) r Radius (m) Re Reynolds number (-) RS t Surface roughness parameter: absolute peak to valley distance (m) U Superficial velocity (m/s) u Velocity of bubble or droplet (m/s) We Weber number (-) z Distance in the z-direction (m)
Design Automation has been in focus of research and application for several decades. This paper aims at establishing the current view of design automation and identification of potential for adoption based on a survey conducted in German speaking countries and a hypothesis based multivariate analysis based on networks. The findings show that design automation is still considered a means of automation of repetitive design tasks and potential for enhanced application exists. The necessity for methods supporting designers for identification and definition of design automation tasks is urged.
Paradigms such as smart factory and industry 4.0 enable the collection of data in enterprises. To enhance decision making in design, computational support that is driven by data seems to be beneficial. With this respect, an identification of data-driven use cases is needed. Still, the state of practice does not reflect the potential of data-driven design in engineering product development. With this respect, a method is proposed addressing the business and data understanding in industrial contexts and corresponding Product Lifecycle Management (PLM) environments. This allows to identify use cases for data-driven design taking into account business processes as well as the related data. In the proposed method, first the main process tasks are analyzed using a SIPOC analysis that is followed by a process decomposition to further detail and highlight corresponding applications using Enterprise Architecture principles. Following this, value stream mapping and design process failure mode effect analysis are used to identify sources of waste and the related causes. With this, a feature analysis of given data is proposed to identify use cases and enable to further use standard data science methods like CRISP-DM. The method is validated using the infrastructure of the Pilotfabrik at TU Vienna. The use case shows the applicability of the method to identify features that influence the cost of a product during the manufacturing without changing the functional specifications. The results highlight that different methods need to be combined to attain a comprehensive business and data understanding. Further, a comprehensive view of the processes is yielded that enables to further identify use cases for data-driven design. This work lays a foundation for future research with respect to data-driven design use cases identification in engineering product development. Keywords: Data-driven-design • PLM • Enterprise architecture • ArchiMate • SysML This work has been partially supported and funded by the Austrian Research Promotion Agency (FFG) via the "Austrian Competence Center for Digital Production" (CDP) under the contract number 854187.
Digital Engineering is an emerging trend and aims to support engineering design by integrating computational technologies like design automation, data science, digital twins, and product lifecycle management. To enable alignment of industrial practice with state of the art, an industrial survey is conducted to capture the status and identify obstacles that hinder implementation in the industry. The results show companies struggle with missing know-how and available experts. Future work should elaborate on methods that facilitate the integration of Digital Engineering in design practice.
The complexity and dynamics of IT landscapes and related PLM strategies of engineering enterprises are continuously growing due to trends such as Industry 4.0 and ever shorting product development cycles. To ensure interoperability, robustness, flexibility and efficiency of the IT systems and PLM, methods are needed that can handle these dynamics and complexities. In this paper, a method is presented that combines principles from enterprise architecture as well as business process mining to enable continuous improvement of PLM processes and the related IT systems. In particular, process mining is applied to validate the alignment of IT systems with related PLM processes. The method is demonstrated using an industrial case study that highlights the requirements from industrial practice and the applicability of the approach for PLM related processes. The method is shown to be particularly beneficial for the enterprise architects to support them with quantitative data as a basis for the design of continuous improvement cycles to make the PLM evolve. Future work will address the application of process mining for PLM related processes with distributed IT systems and the handling of the related complexity.
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