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X-WR-CALNAME:PHD in Industrial Engineering
X-ORIGINAL-URL:https://academics.dii.unipd.it/phd
X-WR-CALDESC:Events for PHD in Industrial Engineering
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DTSTART:20250101T000000
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DTSTART;TZID=UTC:20260904T080000
DTEND;TZID=UTC:20260905T170000
DTSTAMP:20260422T031534
CREATED:20260413T144246Z
LAST-MODIFIED:20260413T145935Z
UID:3255-1788508800-1788627600@academics.dii.unipd.it
SUMMARY:Powder flowability
DESCRIPTION:Course unit contentsThis course provides fundamental knowledge\, theory\, and practical insights for researchers addressing the complex challenges associated with particulate materials and powder flowability. The programme bridges the gap between microscopic particle properties and macroscopic flow behavior\, essential for optimizing industrial processes involving granular media. \nThe course is organized into two core modules. The first section examines the key physical properties and characterization methods for particulate materials\, including particle morphology\, size distribution\, apparent density\, and porosity. This includes a detailed analysis of solid-solid contact interactions and the definition of cohesion. The second section focuses on the mechanics of solids applied to powders\, introducing static and dynamic stress analysis and yield criteria through Mohr-Coulomb models. Students will investigate active and passive stress states and master the Jenike approach for the design of industrial storage units. This integrated approach ensures that doctoral candidates can translate theoretical yield criteria into robust engineering designs\, preventing flow-related failures in industrial handling and storage. \nLearning goalsThe primary objective is to equip PhD students with a deep understanding of powder flowability and the scientific principles governing granular flow. Participants will acquire the skills to measure and interpret flowability data accurately\, understanding the practical implications of these parameters in industrial applications. Furthermore\, the course aims to develop the capability to apply solid mechanics and stress analysis to the design and troubleshooting of powder storage and handling units. \nSuggested readings\nNedderman\, R. M. (1992). Statics and Kinematics of Granular Materials. Cambridge University Press.\nHoldich\, Richard G. (2002). Fundamentals of Particle Technology. Shepshed: Leicestershire\, Midland Information Technology & Publishing.\nSchulze\, D. (2008). Powders and Bulk Solids: Behavior\, Characterization\, Storage. Springer.\nBarletta\, D.\, Poletto\, M.\, & Santomaso\, A. C. (2019). Bulk Powder Flow Characterisation Techniques. In A. Hassanpour\, C. Hare\, & M. Pasha (Eds.)\, Powder Flow: Theory\, Characterisation and Application (pp. 64–146). Royal Society of Chemistry.
URL:https://academics.dii.unipd.it/phd/event/powder-flowability/
LOCATION:Sede-V\, via Venezia 1\, Padova\, Padova\, 35131\, Italy
CATEGORIES:Event
ATTACH;FMTTYPE=image/jpeg:https://academics.dii.unipd.it/phd/wp-content/uploads/sites/58/2026/04/CDII_powder_1-scaled.jpeg
ORGANIZER;CN="PhD Course in Industrial Engineering":MAILTO:dottorato.dii@unipd.it
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20260921T080000
DTEND;TZID=UTC:20260922T170000
DTSTAMP:20260422T031534
CREATED:20260413T151830Z
LAST-MODIFIED:20260421T155401Z
UID:3268-1789977600-1790096400@academics.dii.unipd.it
SUMMARY:Python for numerical heat transfer modeling and building physics
DESCRIPTION:Course unit contentsDespite the rapid rise of data-driven methodologies\, physics-based modeling remains a crucial\, flexible\, and adaptable tool for engineering research. This course provides a practical introduction to transient heat transfer and building physics modeling\, leveraging the Python programming language to bridge theoretical foundations with computational implementation. \nThe programme begins with a brief introduction to Python\, covering data structures\, control loops\, essential libraries\, and data visualization. Students will then progress to developing their first models\, specifically investigating 1D heat transfer through solid elements and the dynamics of radiant panels. The course extends into more complex architectural modeling\, including the development of Resistance-Capacitance (RC) networks for whole-building simulations and an introduction to model calibration techniques. The course concludes with a project discussion\, where students present and refine their simulations of real-world scenarios—such as radiant floor heat flux and transient building behavior—ensuring a deep understanding of the practical guidelines required for high-fidelity physical modeling. \nLearning goalsParticipants will master the basic principles of establishing and solving physics-based systems using both implicit and explicit numerical solution schemes. A primary objective is to develop proficiency in Python for scientific applications\, enabling PhD students to translate physical laws into robust simulation models for transient thermal phenomena. \nSuggested readings\nTransition from MATLAB to Python. https://www.enthought.com/wp-content/uploads/2019/08/Enthought-MATLAB-to-Python-White-Paper_.pdf\nThink Python. https://greenteapress.com/wp/think-python-2e/Physics modeling\nASHRAE Fundamentals. Incropera F.\, De Witt D. Fundamentals of Heat Transfer\, John Wiley&Sons. 1981 or other edition.\nBuilding Physics – Applications in Python. https://www.researchgate.net/publication/353514722_Building_Physics_-_Applications_in_Python\nSolving inverse problems in building physics: an overview of guidelines for a careful and optimal use of data. https://srouchier.github.io/files/2018-enb-review.pdf
URL:https://academics.dii.unipd.it/phd/event/python-for-numerical-heat-transfer-modeling-and-building-physics/
LOCATION:Sede-V\, via Venezia 1\, Padova\, Padova\, 35131\, Italy
CATEGORIES:Event
ATTACH;FMTTYPE=image/jpeg:https://academics.dii.unipd.it/phd/wp-content/uploads/sites/58/2026/04/CDII_python-scaled.jpeg
ORGANIZER;CN="PhD Course in Industrial Engineering":MAILTO:dottorato.dii@unipd.it
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