Client’s Notes The report should be neatly written with proper Harvard style referencing please

Client’s Notes

– 3000 words

– Please tell the writer to focus on the marking rubric given in the end as it’s very important for scoring high marks

– The report should be neatly written with proper Harvard style referencing please

– Also please tell the writer don’t take content from ChatGPT as the university has some special AI detection software developed Implement a wide range of deterministic andprobabilistic system models.2. Demonstrate the relationship between discrete andcontinuous systems.3. Illustrate simple properties of dynamic systems in bothtime and frequency domains.4. Implement system models in Matlab.5. Evaluate the appropriate modelling paradigm(s) todescribe behaviour of real engineering systems. ENGN8120 Systems Modelling S2 2023
School of Engineering/ College of Engineering, Computing & Cybernetics
Modelling assignment
Details
Due date
10am Monday 3td October 2023
Submission
Turnitin on course Wattle site
Format
Report via pdf
Referencing and citation
Harvard or IEEE style
Value
15 % of course total
Length
Up to 3000 words
Participation
Individual
Estimated Workload
18 hours
Learning outcomes
1.
Implement a wide range of deterministic and
probabilistic system models.
2. Demonstrate the relationship between discrete and
continuous systems.
3. Illustrate simple properties of dynamic systems in both
time and frequency domains.
4. Implement system models in Matlab.
5. Evaluate the appropriate modelling paradigm(s) to
describe behaviour of real engineering systems.
Description
The task of creating mathematical models and applying them in various contexts fosters
critical thinking, analytical skills, and the ability to translate theory into practice. Moreover, it
emphasises the significance of accurately representing data and considering all relevant
variables for more comprehensive and reliable models. Ultimately, it prepares you to tackle
real-world challenges, innovate, and contribute meaningful solutions to pressing global issues.
The skills and knowledge gained from this assignment are not only applicable in academic
settings but are also extremely valuable in a wide range of professional engineering and
research roles.
Choose one of the following three topics. You can only get a maximum of 100 marks for this
assignment and if your submission talks to more than one of these topics, we will only mark the
first chosen topic and disregard the remainder of your submission.
Option 1: Photovoltaic (PV) Solar Panel Modelling [100 marks]
Given the increasing deployment of renewable energy generation like solar PV, it is important
to be able to accurately forecast the power and energy generation from these assets. In this
assignment we are investigating the clear sky forecasting of solar PV using whatever
techniques you believe are most appropriate. Please complete the following questions to the
best of your ability:
The Australian National University
ENGN3301 S2 2023 – Reflection
a) What do you believe are the modelling variables that need to be considered when
developing a model for the clear sky forecasting of solar PV power and energy
generation? [10 marks]
b) Based on your answer to (a), develop a mathematical model that allows you to forecast
the instantaneous clear-sky power generation of a solar PV panel of arbitrary area, at
any time of day, in any location on earth. [40 marks]
c) Based on your answer to (b), calculate and plot the instantaneous clear-sky power
generation of a 1m2 solar PV panel for the entire day of the equinox in Canberra. [10
marks]
d) Based on your answer to (c), calculate the clear-sky energy generated by a 1m2 solar
PV panel for the entire day of the equinox in Canberra. [10 marks]
e) If an average home in Canberra consumes 7500kWh per year, and assuming clear-sky
conditions are always present, how large would the solar array have to be in order to
satisfy their energy generation needs? [20 marks]
f) What additional modelling variables would need to be taken into account to develop a
more general model for the forecasting of solar PV when clear-sky conditions may not
be present? [10 marks]
Option 2: Quarter-car Suspension Modelling [100 marks]
Every modern car has a suspension system to make the ride on uneven and bumpy roads more
comfortable. In most modern cars there is one independent suspension system per wheel,
allowing to model only one of these and leading to the so-called quarter-car model. In this
assignment we are investigating the dynamic response of a car suspension system using
whatever techniques you believe are most appropriate. Please complete the following
questions to the best of your ability:
a) What do you believe are the modelling variables that need to be taken into account
when developing a model for the dynamic response of a car suspension model? [10
marks]
b) Based on your answer to (a), develop a mathematical model that allows you to forecast
the instantaneous vertical displacement of the car body for any driving condition and
given an arbitrary road height profile. [40 marks]
c) Based on your answer to (b), calculate and plot the instantaneous vertical displacement
of the car body when the wheel travels over the centre of a round pot-hole of 75cm
diameter and 8cm depth with vertical edges at a constant speed of 30km/h. [10 marks]
d) Based on your answer to (c), calculate the maximal additional vertical acceleration
experienced by the car body while crossing the pot-hole. Do not include gravity itself in
this figure and provide your answer as a fraction of the Earth’s gravitational
acceleration g=9.81m/s2. [10 marks]
e) How big and deep would the pot-hole need to be so that the car body experiences a
maximal additional vertical acceleration of 0.5g? How does this change with different
car velocities? [20 marks]
f) What additional modelling variables would need to be considered to develop a more
general model for a car suspension including the interaction between the four wheels?
[10 marks]
Option 3: Traffic network modelling [100 marks]
Congestion of road networks in large cities is one of the avoidable sources of excess pollution
and emissions that drive human induced climate change. Understanding why congestion occurs
is vital in developing control strategies for traffic lights that reduce congestion. In this
The Australian National University
ENGN3301 S2 2023 – Reflection
assignment we are investigating the flow of traffic through a linear chain of four-way
intersections (think Northbourne Avenue in Canberra). To simplify the investigation, we will only
consider standard passenger cars (no trucks, busses, bicycles, pedestrians, etc.) Please
complete the following questions to the best of your ability:
a) What do you believe are the modelling variables that need to be taken into account
when developing a model for the flow of traffic through a linear chain of four-way
intersections with turning lanes and a complete set of traffic lights at each
intersection? [10 marks]
b) Based on your answer to (a), develop a mathematical model that allows you to forecast
the flow of traffic at any point of the main axis road (i.e. Northbourne Avenue). [40
marks]
c) Based on your answer to (b), calculate and plot the traffic pattern that would occur at a
fixed point on the main axis road in peak hour traffic. [10 marks]
d) Develop a simple way of estimating the CO2-emissions generated by a car travelling
along the main axis road in your traffic model. [10 marks]
e) Based on your answers to (c) and (d), calculate an estimate for the excess CO2emissions generated by the peak hour traffic pattern compared to normal flow of
traffic. [20 marks]
f) What additional modelling variables would need to be taken into account to develop a
more general model that includes multiple types of road users (not only standard cars)?
[10 marks]
Important notes:
•
•
Please detail any assumptions you make to complete this assignment.
Please cite any resources you use to complete this assignment.
References
References and citations should follow the Harvard or IEEE referencing standard. References
should be provided at the end of the document and will not be included in the page limit. For
more information regarding referencing please see the Resources from ANU Academic Skills:
https://www.anu.edu.au/students/academic-skills/academic-integrity/referencing.
Assignment Submission
•
•
You will submit one file (preferably pdf) containing your report using the Turnitin
assignment submission portal on the course Wattle site.
Name your submission according to the following file naming convention.
Naming convention
Example
[UniversityID]_[assignment_name].[file_type]
u5169473_assignment.pdf
Marks and feedback
The assignment will be graded based in the rubric on the following pages.
Marks for the assignment will be released up to two weeks after submission. Feedback will be
available via the Wattle gradebook.
The Australian National University
ENGN3301 S2 2023 – Reflection
Marking Rubric
Criteria
Understanding
of Modelling
Variables
(10%)
Development of
Mathematical
Model
(40%)
Application of
Model to a
Specific Case Calculation and
Plot
(10%)
Further
Application of
Model to a
Different Case Calculation
(10%)
High-Distinction
(80-100)
Distinction
(70-79)
Credit
(60-69)
Pass
(50-59)
Fail
(0-49)
Identifies and
insightfully discusses all
key variables. Provides
comprehensive
understanding of their
importance and impact.
Correctly identifies all key
variables. Good
understanding of their
importance and impact is
demonstrated.
Major variables are
identified, but
explanations lack detail or
contain some errors.
Some variables are
identified, but
explanations are missing
or incorrect.
Omits major variables, or
there are significant
errors in their
identification or
explanation.
Presents an innovative,
well-structured, and
accurate model.
Articulates how key
variables are
incorporated and their
interrelations.
Model is correctly
constructed with minor
errors or omissions. Key
variables are mostly
accurately incorporated.
Model is vaguely
constructed, lacks detail,
or contains errors, and
incorrectly uses some
variables.
Model contains errors,
omissions, or incorrect
use of variables, and lacks
a clear logical basis.
Model contains significant
errors, omissions, or
incorrect use of variables,
lacking a clear logical or
mathematical basis.
Precisely executes
calculations. Presents a
clear, accurate, and
aesthetically compelling
plot that represents data
correctly.
Minor errors exist in
calculations. Plot
represents data correctly
but lacks some aesthetic
consideration.
Errors exist in
calculations. The plot is
unclear or improperly
labelled.
Some effort has been
made to complete the
calculations and plot, but
the results are incorrect
or misleading.
Major errors exist in
calculations. The plot is
unreadable or misleading.
Demonstrates
exceptional
understanding by
accurately applying
model to a different
context. Insightful
explanation of results.
Good understanding is
shown by applying model
to a different context with
minor errors. Explanation
of results is clear.
Errors exist in applying
the model to a different
context. Explanation is
vague or contains errors.
Some effort has been
made to apply model to a
different context, but the
results are incorrect, or
the explanation is poor.
Major errors exist in
applying the model to a
different context.
Explanation is largely
missing or incorrect.
The Australian National University
4
ENGN3301 S2 2023 – Reflection
Criteria
Model Extension
and New
Calculations
(20%)
Identification of
Additional
Modelling
Variables for a
More General
Model
(10%)
High-Distinction
(80-100)
Distinction
(70-79)
Credit
(60-69)
Pass
(50-59)
Fail
(0-49)
Provides a sophisticated
extension to the model.
Demonstrates deep
understanding with
insightful calculations
and clear, justified
reasoning.
Extends the model well,
with minor errors. Shows
good understanding with
clear calculations and
justified reasoning.
Model extension contains
errors and lacks detail.
Extended calculations are
vaguely explained.
Some effort has been
made to extend the model
and make extended
calculations, but the
results are incorrect or
poorly justified.
The model extension
contains major errors or
omissions, or the
extended calculations are
significantly incorrect.
Insightfully identifies all
additional variables
needed for a more
general model and
provides exceptional
explanation.
Correctly identifies all
additional variables for a
more general model. Good
explanation provided.
Some additional variables
are identified, but the
explanation is vague or
contains errors.
Some effort has been
made to identify
additional variables, but
many are missing or
poorly explained.
Many additional variables
are missing, or there are
significant errors in their
identification or
explanation.
The Australian National University
5