| Code |
18151
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| Year |
2
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| Semester |
S1
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| ECTS Credits |
6
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| Workload |
PL(15H)/T(30H)/TP(15H)
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| Scientific area |
MECÂNICA COMPUTACIONAL
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Entry requirements |
N.A.
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Learning outcomes |
-Understand the fundamental principles and characteristics of Digital Twins, including their historical development and importance in modern industries.
-Develop mathematical models for continuous and discrete systems, implementing simulations to analyze system behaviors.
-Explore the role of technologies such as the Internet of Things (IoT), cloud computing, edge computing, and artificial intelligence in the development and operation of Digital Twins.
-Understand the types of data used in Digital Twins, such as geometric, behavioral, historical, synthetic, and real-time data, applying data analysis techniques for informed decision-making.
-Learn visualization techniques to interact with and interpret Digital Twin simulations.
-Promote teamwork skills and the ability to solve complex problems in industrial scenarios.
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Syllabus |
-Introduction to Digital Twins: Definition and fundamental concepts, Historical context and evolution, Importance in digital transformation and various industries.
-Mathematical Foundations and Modeling: Mathematical foundations for simulations, Modeling of systems (continuous and discrete), Digitalization of models, Simulation of digital models.
-Enabling Technologies: Sensor technologies and IoT, Cloud and edge computing, Applications of machine learning and artificial intelligence.
-Data Management and Analysis: Types of data in Digital Twins, Data acquisition, storage, and processing, Descriptive, diagnostic, predictive, and prescriptive analytics.
-Visualization Techniques
-Implementation and Case Studies
-Future Trends and Challenges
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Main Bibliography |
-Sabri, S., Lee, N., Isaacs, D., & Alexandridis, K. (2024). Digital Twin Fundamentals and Applications. Springer Nature.
-Crespi, N., Drobot, A. T., & Minerva, R. (2023). "The Digital Twin: What and Why?" In The Digital Twin (pp. 3–20). Springer InternationalPublishing. https://doi.org/10.1007/978-3-031-21343-4_1
-Grieves, M. (2023). "Digital Twins: Past, Present, and Future." In The Digital Twin (pp. 97–121). Springer International Publishing.https://doi.org/10.1007/978-3-031-21343-4_4
-Chaudhary, G., Khari, M., & Elhoseny, M. (2021). Digital Twin Technology. CRC Press.https://books.google.com/books?id=5AxIEAAAQBAJ
-Digital Twin Consortium. "Components of Digital Twins Reference Architecture." https://www.digitaltwinconsortium.org/
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Teaching Methodologies and Assessment Criteria |
Assessment criteria:
TAS – Analysis and synthesis of bibliographic research (15%)
Ex – Exercises (15%)
LAB – Laboratory (20%)
PRO – Project (30%)
PR – Evaluation test (20%)
Final Grade Calculation:
CF = TAS + Ex + LAB + PRO + PR
Attendance and eligibility for the course during the teaching/learning period require a final grade (CF) of 9.5 or higher, considering the
following conditions:
All components listed in the assessment criteria must be completed.
Minimum grade in the evaluation test: PRmin ? 6.
Minimum grade in the laboratory component: LABmin ? 10.
Improving the grade obtained in continuous assessment requires taking the final exam. The final grade is determined using the same
calculation formula, considering the grades obtained in the different assessment components.
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Language |
Portuguese. Tutorial support is available in English.
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