Electromagnetism

1.-To understand the basic phenomena of electromagnetism and their physical-mathematical models.
2.-To exactly solve particular cases with analytical techniques or under appropriate physical-mathematical approaches.
3.-To mathematically formulate problems in order to solve them numerically.

1.-Mathematical requirements: field theory, distributions and functional spaces.
2.-General concepts about waves. Examples.
3.-Maxwell's equations in vacuum.
4.-Maxwell equations in material regions.
5 –Electrostatics.
6.-Continuous electrical current.
7.-Magnetostatics.
8.-Quasi-static approximation. Harmonic regime. Electromagnetic induction. Eddy currents.
9.- Wave equation in free space and radiated field.
10. Radiation diagram and antenna parameters
11.- Introduction to linear and aperture antennas.

1. A. Bermúdez, D. Gómez, P. Salgado, Mathematical Models and Numerical Simulation in Electromagnetism. UNITEXT, Vol. 74. Springer. 2014
• A. Bossavit, Computational Electromagnetism. Variational Formulations.Complementarity, Edge Elements. Academic Press. San Diego, CA, 1998.
2. M. Cessenat, Mathematical Methods in Electromagnetism. World Scientific. 1996.
3. T. A. Johnk, Engineering Electromagnetic Fields and Waves, Springer, 2001.
4. P. Monk, Finite Element Methods for Maxwell’s Equations, Clarendon Press. Oxford. 2003.
5. J. C. Nédélec, Acoustic and Electromagnetic Equations, Springer, 2001.
6. D. Popovic, Introductory Engineering Electromagnetics. Addison Wesley, 1971.
7. B. Reece and T. W. Preston, Finite Elements Methods in Electrical Power Engineering, Oxford University Press, Oxford, 2000.
8. P. P. Silvester and R. L. Ferrari, Finite Elements for Electrical Engineers, Cambridge University Press, Cambridge, 1996.
9. C.A. Balanis, Antenna Theory: Analysis and Design, John Wiley and Sons, 4ª ed. 2016.
10. W. L. Stutzman, G. A. Thiele, Antenna Theory and Design, John Wiley and Sons, 3ª ed. 2013.

Basic:

CG1: To have knowledge that provide a basis or opportunity for originality in developing and / or applying ideas, often within a research context, knowing how to translate industrial needs in terms of R & D in the field of mathematics Industrial.
CG2: To be able to apply the acquired knowledge and abilities to solve problems in new or unfamiliar environments within broader contexts, including the ability to integrate multidisciplinary R & D in the business environment.
CG4: To have the ability to communicate the findings to specialist and non-specialist audiences in a clear and unambiguous way.
CG5: To have the appropriate learning skills to enable them to continue studying in a way that will be largely self-directed or autonomous, and also to be able to successfully undertake doctoral studies.


Specific:

CE1: To acquire a basic knowledge in an area of Engineering / Applied Science, as a starting point for an adequate mathematical modelling, using well-established contexts or in new or unfamiliar environments within broader and multidisciplinary contexts.
CE2: To model specific ingredients and make appropriate simplifications in the model to facilitate their numerical treatment, maintaining the degree of accuracy, according to previous requirements.
CE5: To be able to validate and interpret the results, comparing them with visualizations, experimental measurements and functional requirements of the physical engineering system.

Modelling specialization:

CM1: To be able to extract, using different analytical techniques, both qualitative and quantitative models.


Teaching methodologies:
Classes: CG1-CG2-CG3-CG4-CG5 CE1-CE2-CE3 CM1
Exercises solving: CG1-CG2-CG3-CG4-CG5 CE1-CE2-CE3 CM1

1.-Planning for the contents of each class.
2.-Delivery of teaching materials in pdf
3.-Explanation on electronic blackboard (lecture).
4.-Solving exercises
5.-Use of telematic resources for supplementary activities


The USC Virtual Campus will be used.

-Exercises and practices will be proposed, presented and evaluated, contributing to 30% of the qualification.
-An exam will also be carried out for all students, which will represent the remaining 70% of the final grade.

Assessment activities (competences):
Exercises and practices: CG1-CG2-CG3-CG4-CG5 CE1-CE2-CE3 CM1
Exam: CG1-CG2-CG3-CG4-CG5 CE1-CE2-CE3 CM1

Class hours: theoretical: 32; practices: 10.
- Student work: 73.
- Assessment hours: 5.

Total volume of work: 32 + 10 + 73 + 5 = 120 hours.

Class hours: theoretical: 32; practices: 10.
- Student work: 73.
- Assessment hours: 5.

Total volume of work: 32 + 10 + 73 + 5 = 120 hours.

Contingency plan to adapt this guide to the “Bases” document which develops a safe face-to-face teaching environment on the 2020-2021 academic year (approved by the USC Government Council in a regular session held on June 19, 2020).

1. The teaching methodology and the evaluation system described above would correspond to Scenario 1: adapted normality (without restrictions on physical attendance) following the USC Guidelines for the development of safe classroom teaching.

2. If the scenario were Scenario 2: distancing (partial restrictions on physical presence), both headings should adapt to said Guidelines: both the expository and interactive classes will be taught preferably from the classroom. They can also be carried out virtually, depending on the circumstances. The tutorials will be telematic.
The evaluation system would not change except for the final exam that would have to be done electronically using the Virtual Campus and the Teams platform.

3. If the scenario were Scenario 3, both expository and interactive teaching will be completely telematic with synchronous mechanisms, through the Microsoft Teams application. The scheduling of tutorials will be done electronically.
The evaluation system would not change except for the final exam that would have to be done electronically using the Virtual Campus and the Teams platform.