20 Credit Modules

Prerequisites

There are no pre-requisites for this module to be added

Description of module content:

This module equips students with advanced skills in computational and analytical techniques essential for addressing complex engineering challenges in the design and optimization of smart places. Students will develop a solid foundation in mathematical modelling, programming, and numerical methods, enabling them to devise and evaluate solutions for real-world engineering problems.The module begins by introducing a comprehensive suite of computational and analytical methods, fostering a deep understanding of their theoretical underpinnings and practical applications. Through hands-on programming exercises, students will learn to implement these methods effectively, bridging the gap between conceptual knowledge and real-world practice.Mathematical modelling will play a central role, with a focus on translating complex engineering scenarios into solvable mathematical representations. Students will engage with case studies relevant to smart infrastructure, transportation systems, sustainable energy, and urban planning to hone their problem-solving skills.A significant emphasis will be placed on numerical methods, empowering students to select, implement, and critically evaluate the performance of algorithms for solving engineering problems. The module will guide students in applying these methods to optimize processes, analyze data, and simulate dynamic systems, ensuring solutions are robust, efficient, and applicable to the evolving demands of smart places.By the end of this module, students will have cultivated a holistic skillset in computational modelling and numerical problem-solving, preparing them to tackle interdisciplinary engineering challenges with confidence and innovation.

Learning Outcomes for module:

Upon completion of this module you will be able to

LO1: Demonstrate knowledge of and competency in a wide range of computational and analytical methods.

LO2: Apply the principles of mathematical modelling to engineering problems.

LO3: Apply programming skills effectively and implement computational and analytical methods in practice.

LO4: Select, implement and critically appraise the performance of numerical methods for solving engineering problems.

Full Details of Teaching and Assessment

2024/5, Trimester 1, In Person,

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Occurrence:	001
Primary mode of delivery:	In Person
Location of delivery:	MERCHISTON
Partner:
Member of staff responsible for delivering module:	Stathis Tingas
Module Organiser:

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activity	Learning & Teaching Activity	NESH (Study Hours)	NESH Description
Face To Face	Tutorial	20	Each week the students attend a two-hour tutorial. These tutorials offer students an opportunity to apply the theoretical concepts learned in the lectures by engaging in hands-on programming and tackling real-world engineering problems. These sessions emphasize practical implementation and problem-solving, reinforcing the connection between theory and practice.
Online	Guided independent study	160	Guided independent study
Face To Face	Lecture	20	Each week the students attend a two-hour lecture. These lectures focus on developing students' theoretical knowledge, covering advanced topics in mathematical modeling, computational techniques, and engineering principles. These sessions provide the foundational understanding necessary for analyzing complex systems and preparing for practical problem-solving.
	Total Study Hours	200
	Expected Total Study Hours for Module	200

Assessment
Type of Assessment	Weighting %	LOs covered	Week due	Length in Hours/Words	Description
Report	50	1~2~3~4	Week 7	, WORDS= 1500 words	This assessment requires students to develop mathematical models and perform computational simulations to analyse and optimize the performance of a complex engineering system, with findings documented in a detailed report and supported by a submission of the implemented code.AHEP4 mapping:The construction of mathematical models to represent engineering systems demonstrates the application of advanced mathematics and engineering principles to solve complex problems [M1]. Evaluating the behavior of systems under varying parameters involves formulating and analyzing complex problems using first principles, with an awareness of uncertainties and limitations in the techniques employed [M2]. Utilizing numerical methods such as integration, stability analysis, and optimization illustrates the selection and application of computational techniques to model and solve engineering challenges [M3]. Simulating the performance of systems under dynamic and variable conditions highlights the use of integrated approaches to address multi-faceted engineering problems [M6]. Creating and interpreting plots, graphs, and visual data representations demonstrates a systematic approach to analysing and presenting results derived from complex computational models [M6]. The implementation of algorithms and programming for problem-solving showcases the integration of mathematical and computational principles into practical engineering applications [M1, M3]. Assessing system responses to external changes or uncertainties demonstrates the use of engineering judgment to work with incomplete or variable data, discussing the implications of system dynamics [M2, M6]. Comparing and contrasting system performance under different scenarios reflects the ability to evaluate engineering methods and solutions critically, taking into account their limitations and potential improvements [M2, M3].
Report	50	1~2~3~4	Week 13	, WORDS= 1500 words	This assessment involves the application of computational and numerical techniques to model, simulate, and assess the performance of an adaptive engineering system, with results presented in a written report and accompanied by the corresponding code implementation.AHEP4 mapping:The construction of mathematical models to represent engineering systems demonstrates the application of advanced mathematics and engineering principles to solve complex problems [M1]. Evaluating the behavior of systems under varying parameters involves formulating and analyzing complex problems using first principles, with an awareness of uncertainties and limitations in the techniques employed [M2]. Utilizing numerical methods such as integration, stability analysis, and optimization illustrates the selection and application of computational techniques to model and solve engineering challenges [M3]. Simulating the performance of systems under dynamic and variable conditions highlights the use of integrated approaches to address multi-faceted engineering problems [M6]. Creating and interpreting plots, graphs, and visual data representations demonstrates a systematic approach to analysing and presenting results derived from complex computational models [M6]. The implementation of algorithms and programming for problem-solving showcases the integration of mathematical and computational principles into practical engineering applications [M1, M3]. Assessing system responses to external changes or uncertainties demonstrates the use of engineering judgment to work with incomplete or variable data, discussing the implications of system dynamics [M2, M6]. Comparing and contrasting system performance under different scenarios reflects the ability to evaluate engineering methods and solutions critically, taking into account their limitations and potential improvements [M2, M3].
Component 1 subtotal:	100
Component 2 subtotal:	0
Module subtotal:	100

2024/5, Trimester 2, IN PERSON,

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Indicative References and Reading List - URL:

ELE11117 Modelling and Computation for Smart Places

Core Module Information
Module title:	Modelling and Computation for Smart Places
SCQF level:	11:
SCQF credit value:	20.00
ECTS credit value:	10
Module code:	ELE11117
Module leader:	Stathis Tingas
School	School of Computing, Engineering and the Built Environment
Subject area group:	Engineering and Mathematics

Edinburgh Napier University

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