Coursework Title: Individual project
Module Title: Multidisciplinary Design & Engineering Optimisation
Module Code: KB7043
Module Tutor: Dr Madeleine L Combrinck
Academic Year: 2019-20
Dates and Mechanisms for Assessment Submission and Feedback
Date of hand out to students: 27 January 2020
Mechanism to be used to disseminate to students: During lecture and on black board (elp)
Date and Time of Submission by Student: 23 April 2020 23:59
Mechanism for Submission of Work by Student: Online on black board (elp).
No hard copies or emailed work will be accepted.
Date by which Work, Feedback and Marks will be returned to Students:
25 May 2020 at 08:00
Mechanism(s) for return of assignment work, feedback and marks to students: Online via
Task: Choose a design optimisation problem from the attached list of design problems. Write
a report with no more than 7000 words and no more than 15 A4 pages in the main body.
Submit the report Via ELP before 23 April 2020 23:59.
Learning Outcomes assessed in this assessment: (from the Module Descriptor)
Appraise key features of modern engineering design concepts, theories and methods and
develop critiques of them
Plan the design optimisation processes for complex engineering design problems
Conduct essential calculations for reliability driven design problems
Formulate for a given design problem the corresponding optimisation problem,
identifying the best applicable search method and carrying out essential calculations to
find the optimum solution
Carry out design under uncertainties for a given problem, making critical decisions and
performing essential calculations
Additional Instructions to students: This is an individual project.
Students will make use of MATLAB for their coding.
The report will be typed using the template provided, with single line spacing, 11pt Calibri
Failure to submit: The University requires all students to submit assessed coursework by the
deadline stated in the assessment brief. Where coursework is submitted without approval after
the published hand-in deadline, penalties will be applied as defined in the University Policy on
the Late Submission of Work. https://www.northumbria.ac.uk/static/5007/arpdf/lateappr
Assessment Criteria/Mark Scheme:
|Introduction||Overview||Provide an introduction to the problem
selected, the aims and objects of this study
and state the relevance in industry.
|Carry out a literature survey on the theory
and application relevant to the selected
|Formulate the selected problem verbally and
mathematically in the form of an
optimisation problem. Elaborate on the
identification of design qualities, selection of
design objective(s), design variables, type of
constraints and type of the optimisation
|Select an apporpriate optimization method
(Genetic Algorithm, Particle Swarm or
Simulated Annealing) for solving the optimi
sation problem. Justify the rationale behind
the selected method and discuss the selected
constraint handling method, fitness
definition, and objective evaluation.
|Implementation||Implement the problem formulation in the
optimization code and briefly discuss the
implementation in the main body.
Provide the MATLAB code in Appendix A.
|Solve the optimisation problem and prove
the optimality of the solution. Investigate
effect of changes in the controlling
|Identify all sources of uncertainties in the
selected design optimisation problem.
|Quantify/Approximate the level and
distribution of uncertainties for each
uncertain parameter identified above.
|Write a Monte Carlo code to investigate the
effect of uncertainties on the performance
and the robustness of the optimal solution
Provide the MATLAB code in Appendix B.
|Conclusion||Summary||Conclude the report with the major findings.||5%|
|Neatness of the document, spelling and
grammar, figures and tables, referencing,
making use of the template.
|Keeping within the word and page limit.||10%|
Referencing Style: British Standard or Harvard
Expected size of the submission: no more than 7,000 words or 15 A4 pages, with single line
spacing, 11pt Calibri (Body) font.
Assignment weighting: This assignment is worth 50% of the module marks
Academic Integrity Statement: You must adhere to the university regulations on academic
conduct. Formal inquiry proceedings will be instigated if there is any suspicion of plagiarism or
any other form of misconduct in your work. Refer to the University’s Assessment Regulations
for Northumbria Awards if you are unclear as to the meaning of these terms. The latest copy is
available on the University website.
Design Optimisation Problems
Select ONE of the options below and follow the instructions given on the assignment brief.
|1||Optimal sizing of a standalone Wind-PV-Battery-Diesel hybrid renewable
For an arbitrary site with known load and resource profile, find optimum size of
each component (including inverter/converter) leading to minimum levelised cost
of energy subject to a series of constraints including a number of arbitrary end
Maheri, Alireza (2014) Multi-objective design optimisation of
standalone hybrid wind-PV- diesel systems under uncertainties.
Renewable Energy, 66. pp. 650-661
|2||Design optimisation of a flat finned heat exchanger
For an arbitrary capacity (heat transfer rate in W), ambient temperature and
maximum allowable temperature, find the optimal material and size for the
finned heat sink below leading to minimum cost.
Articles published in journals Heat and Mass Transfer, Applied
Thermal Engineering, International Journal of Refrigeration
(keyword: flat finned heat exchanger).
|3||Design optimisation of an adaptive passive beam vibration absorber
For an arbitrary set of data (beam length, cross-section and material), find the
optimal configuration and characteristics of a string-mass absorber that maximises
the absorber operation range.
Acar M.A. and Yilmaz C (2013) Design of an adaptive–passive
dynamic vibration absorber composed of a string–mass system
equipped with negative stiffness tension adjusting mechanism. Journal
of Sound and Vibration, 332. pp. 231–245
|4||Design optimisation of a nanofluid flat solar collector
Find the optimum configuration (tube type, tube size, tube surface roughness,
type of nanoparticle, size of nanoparticle, mass flow rate, tube distribution
configuration, glazing type and size, insulation size and type) of a nanofluid flat
solar collector which maximises the efficiency per unit area.
Articles published by Omid Mahian (keyword: nanofluid) in journals
International Journal of Heat and Mass Transfer, Energy Conversion and
Management, Experimental Thermal and Fluid Science.
|5||Design optimisation of hybrid photovoltaic–thermal collectors
Find the optimum configuration (see figure below) of a hybrid photovoltaic–thermal
collector integrated in a domestic hot water heating system with the objective of
Vera JT, Laukkanen T, Siren K (2014) Multi-objective optimization of
hybrid photovoltaic– thermal collectors integrated in a DHW heating
system. Energy and Buildings, 74. pp. 78–90
|6||Select your own optimization problem.
Students are permitted to propose a topic of their own choosing should none
of the above appeal to them.
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