İzmir Ekonomi Üniversitesi
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  • FACULTY OF ENGINEERING

    Department of Mechanical Engineering

    FENG 346 | Course Introduction and Application Information

    Course Name
    Numerical Methods for Engineers II
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    FENG 346
    FALL
    3
    0
    3
    6

    Prerequisites FENG 345 (To get a grade of at least FD)
    Course Language İngilizce
    Course Type Required (Core Course)
    Course Level First Cycle
    Mode of Delivery
    Teaching Methods and Techniques of the Course -
    National Occupational Classification Code -
    Course Coordinator
    • Dr. Öğr. Üyesi Musa Özgün Güleç
    Course Lecturer(s)
    • Dr. Öğr. Üyesi Musa Özgün Güleç
    Assistant(s) -
    Course Objectives The course objectives are to provide the central ideas behind algorithms for the numerical solution of differentiable optimization problems by presenting key methods for both unconstrained and constrained optimization and providing theoretical justification as to why they succeed. Additionally, it is aimed to teach the computational tools available to solve optimization problems on computers once a mathematical formulation has been found.
    Learning Outcomes The students who succeeded in this course;
    Name Description PC Sub * Contribution Level
    1 2 3 4 5
    LO1 Define an optimization problem with the objective function and related constraints (equality and/or inequality) in the standard form. 2 X
    LO2 Solve constrained and/or unconstrained optimization problems using analytical methods and graphical approaches. 1.6 X
    LO3 Solve linear optimization problems with linear programming method. 1.6 X
    LO4 Determine the numerical solution of a constrained and/or unconstrained optimization problem using conventional search techniques using MATLAB/Octave (or other tools and programming languages). 1.4 X
    LO5 Solve an optimization problem using modern search techniques / evolutionary algorithms. 1.4 X
    Course Description This course will cover the place and importance of optimization in engineering, basic definitions and facts about optimization problems, analytical and graphical solutions to linear, nonlinear, constrained, and unconstrained optimization problems, and solutions to optimization problems using conventional and modern/evolutionary search techniques.
    Related Sustainable Development Goals
    Quality Education

     



    Course Category

    Core Courses
    X
    Major Area Courses
    Supportive Courses
    Media and Managment Skills Courses
    Transferable Skill Courses

     

    WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

    Week Subjects Required Materials Learning Outcome
    1 Introduction to optimization, its place in engineering, basic definitions, and facts. Textbook 1: Chapter 1 LO1
    2 Analytical solutions of unconstrained optimization problems Textbook 1: Chapter 4 LO2
    3 Analytical solutions of equality constrained optimization problems Textbook 1: Chapter 4 LO2
    4 Analytical solutions of inequality constrained optimization problems Textbook 1: Chapter 4 LO2
    5 Solving optimization problems with the graphical method. Textbook 1: Chapter 3 LO2
    6 Convex problems Textbook 1: Chapter 4 LO2
    7 Linear Programming Textbook 1: Chapter 8 LO3
    8 Midterm Exam -
    9 Conventional numerical methods for one-dimensional optimization problems Textbook 2: Chapter 5 LO4
    10 Conventional numerical methods for one-dimensional optimization problems Textbook 2: Chapter 5 LO4
    11 Conventional numerical methods for multidimensional optimization problems Textbook 2: Chapter 6 LO4
    12 Conventional numerical methods for multidimensional optimization problems Textbook 2: Chapter 6 LO4
    13 Solving optimization problems via evolutionary algorithms Textbook 2: Chapter 13 LO5
    14 Solving optimization problems via evolutionary algorithms Textbook 2: Chapter 13 LO5
    15 Course review -
    16 Final exam -

     

    Course Notes/Textbooks -
    Suggested Readings/Materials -

     

    EVALUATION SYSTEM

    Semester Activities Number Weighting LO1 LO2 LO3 LO4 LO5
    Homework / Assignments 1 15 X X
    Midterm 1 30 X X X
    Final Exam 1 40 X X X X X
    Quizzes / Studio Critiques 1 15 X X
    Total 4 100

     

    ECTS / WORKLOAD TABLE

    Semester Activities Number Duration (Hours) Workload
    Participation 16 3 48
    Theoretical Course Hours - - -
    Laboratory / Application Hours - - -
    Study Hours Out of Class 14 2 28
    Field Work - - -
    Quizzes / Studio Critiques 1 22 22
    Portfolio - - -
    Homework / Assignments 1 22 22
    Presentation / Jury - - -
    Project - - -
    Seminar / Workshop - - -
    Oral Exams - - -
    Midterms 1 26 26
    Final Exam 1 34 34
        Total 180

     

    COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

    # PC Sub Program Competencies/Outcomes * Contribution Level
    1 2 3 4 5
    1

    Engineering Knowledge: Knowledge of mathematics, science, basic engineering, computation, and related engineering discipline-specific topics; the ability to apply this knowledge to solve complex engineering problems.

    1

    Mathematics

    2

    Science

    3

    Basic Engineering

    4

    Computation

    LO5 LO4
    5

    Related engineering discipline-specific topics

    6

    The ability to apply this knowledge to solve complex engineering problems

    LO3 LO2
    2

    Problem Analysis: Ability to identify, formulate and analyze complex engineering problems using basic knowledge of science, mathematics and engineering, and considering the UN Sustainable Development Goals relevant to the problem being addressed.

    LO1
    3

    Engineering Design: The ability to devise creative solutions to complex engineering problems; the ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions.

    1

    Ability to design creative solutions to complex engineering problems

    2

    Ability to design complex systems, processes, devices or products to meet current and future needs, considering realistic constraints and conditions

    4

    Use of Techniques and Tools: Ability to select and use appropriate techniques, resources, and modern engineering and computing tools, including estimation and modeling, for the analysis and solution of complex engineering problems, while recognizing their limitations.

    5

    Research and Investigation: Ability to use research methods to investigate complex engineering problems, including literature research, designing and conducting experiments, collecting data, and analyzing and interpreting results.

    1

    Literature research for the study of complex engineering problems

    2

    Designing experiments

    3

    Ability to use research methods, including conducting experiments, collecting data. analyzing and interpreting results

    6

    Global Impact of Engineering Practices: Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals; awareness of the legal implications of engineering solutions.

    1

    Knowledge of the impacts of engineering practices on society, health and safety, economy, sustainability, and the environment, within the context of the UN Sustainable Development Goals

    2

    Awareness of the legal implications of engineering solutions

    7

    Ethical Behavior: Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility; awareness of being impartial, without discrimination, and being inclusive of diversity.

    1

    Acting in accordance with the principles of the engineering profession, knowledge about ethical responsibility ethical responsibility

    2

    Awareness of being impartial and inclusive of diversity, without discriminating on any subject

    8

    Individual and Teamwork: Ability to work effectively, individually and as a team member or leader on interdisciplinary and multidisciplinary teams (face-to-face, remote or hybrid).

    1

    Ability to work individually and within the discipline

    2

    Ability to work effectively as a team member or leader in multidisciplinary teams (face-to-face, remote or hybrid)

    9

    Verbal and Written Communication: Taking into account the various differences of the target audience (such as education, language, profession) on technical issues.

    1

    Ability to communicate verbally

    2

    Ability to communicate effectively in writing

    10

    Project Management: Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.

    1

    Knowledge of business practices such as project management and economic feasibility analysis

    2

    Awareness of entrepreneurship and innovation

    11

    Lifelong Learning: Lifelong learning skills that include being able to learn independently and continuously, adapting to new and developing technologies, and thinking questioningly about technological changes.

    *1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest


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