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1 – 3 of 3Armando Di Meglio, Nicola Massarotti and Perumal Nithiarasu
In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the…
Abstract
Purpose
In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.
Design/methodology/approach
To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.
Findings
The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.
Originality/value
The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.
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Mohammad Ghalambaz, Mikhail A. Sheremet, Mohammed Arshad Khan, Zehba Raizah and Jana Shafi
This study aims to explore the evolving field of physics-informed neural networks (PINNs) through an analysis of 996 records retrieved from the Web of Science (WoS) database from…
Abstract
Purpose
This study aims to explore the evolving field of physics-informed neural networks (PINNs) through an analysis of 996 records retrieved from the Web of Science (WoS) database from 2019 to 2022.
Design/methodology/approach
WoS database was analyzed for PINNs using an inhouse python code. The author’s collaborations, most contributing institutes, countries and journals were identified. The trends and application categories were also analyzed.
Findings
The papers were classified into seven key domains: Fluid Dynamics and computational fluid dynamics (CFD); Mechanics and Material Science; Electromagnetism and Wave Propagation; Biomedical Engineering and Biophysics; Quantum Mechanics and Physics; Renewable Energy and Power Systems; and Astrophysics and Cosmology. Fluid Dynamics and CFD emerged as the primary focus, accounting for 69.3% of total publications and witnessing exponential growth from 22 papers in 2019 to 366 in 2022. Mechanics and Material Science followed, with an impressive growth trajectory from 3 to 65 papers within the same period. The study also underscored the rising interest in PINNs across diverse fields such as Biomedical Engineering and Biophysics, and Renewable Energy and Power Systems. Furthermore, the focus of the most active countries within each application category was examined, revealing, for instance, the USA’s significant contribution to Fluid Dynamics and CFD with 319 papers and to Mechanics and Material Science with 66 papers.
Originality/value
This analysis illuminates the rapidly expanding role of PINNs in tackling complex scientific problems and highlights its potential for future research across diverse domains.
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Amy L. Jansen and Alice Wieland
This assignment is designed to enhance resilience among students in leadership courses. It leverages the US Army’s Master Resilience Training (MRT) framework and positive…
Abstract
Purpose
This assignment is designed to enhance resilience among students in leadership courses. It leverages the US Army’s Master Resilience Training (MRT) framework and positive psychology to develop resiliency skills.
Design/methodology/approach
A three-part experiential workshop integrates academic readings (providing a foundation of resilience concepts), explores the influence of personal identities on leadership and connects leadership skills with resilience concepts.
Findings
Participants reflect on self-awareness tools and positive psychology and create personalized action plans. Participants' resilience skills are enhanced with their personalized resiliency plan.
Practical implications
The program provides a structured approach to resilience training, which can be integrated into university curriculums. Students gain self-awareness and psychological tools to manage challenges, which are valuable for personal growth and professional development. There is a persistent gender gap in leadership, and for women to attain greater parity in leadership positions, resilience skills are imperative. By focusing on identity-related factors, the program prepares future leaders for challenges in attaining leadership positions.
Originality/value
This program is uniquely tailored for students aspiring to leadership positions, with an emphasis on the role of identity, such as gender, in leader emergence and overcoming related challenges.
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