Category: Engineering

I have a group project and my part is to write 450 / 500 words about limitiaions and ethical considerations of generative AI in project management. please do not us AI to copy and pase the work. Also references is required and Presents logical, well-structured arguments with critical evaluation limitations, and ethical considerations. Also, demonstrates depth, insight, and relevance supported by high quality evidence

School of Engineering, Physics & Mathematics

Faculty of Science and Environment

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Coursework Specification

Module Information

Module Code & Title

KB4044 & Thermodynamics

Module Leader

Dr Yolanda Sanchez Vicente

Assessment Component Number & Weighting

001 & 30%

Coursework Title

Residential heating analysis

Academic Year and Semester(s)

2025-26 and SEM2

Coursework Submission and Feedback

Release Date of Coursework Specification to Students

Week commencing 09

th February 2026

Mechanism Used for Dissemination & Submission of Coursework Specification to Students

Assessment and Submission folder on Blackboard module (eLP)

Date and Time of Submission of Coursework by Students

19 March 2026 before 23:59

Marks and feedback will be returned to students within 20 working days of the deadline.

SEPM | Learning and Teaching | Page 2 of 6

Assessment Details

Module Learning Outcomes (MLOs) Assessed by Coursework

Knowledge & Understanding:

MLO1. Apply knowledge and understanding of scientific principles and methodology to solve well-defined

thermodynamic problems.

Intellectual / Professional skills & abilities:

MLO2. Use appropriate computational and analytical techniques to model well-defined thermodynamics

problems.

Coursework Overview

In this assignment, you will analyse two commonly used residential heating systems by applying

thermodynamic principles. Your analysis should include the determination of the energy input for each system,

the evaluation of the operating costs, the assessment of carbon footprint, and the recommendation of system

improvements.

Coursework Tasks to be Completed by Students

Table 1 shows a list of the most common residential heating options. According to Table 1, two heating

systems will be assigned to you, depending on your student ID’s last digit (S). For example, if your ID

number is 12345678, then S=8, so the assigned two heating systems are Heat Pumps and Fireplaces. You

will analyse these systems according to the principles of thermodynamics.

Table 1. Assignation of residential heating system. S is the last digit of your student number

S Heating Systems

1 Gas Boilers. Including Combi Boilers.

Furnaces

2 Heat Pumps.

Gas-Fired Space Heaters

3 Electric Heaters.

Wood-Burning and Pellet Stoves.

4 Fireplaces

Gas Boilers- Including Combi Boilers

5 Heat Pumps

Gas Boilers- Including Combi Boilers.

6 Electric Heaters.

Gas Boilers- Including Combi Boilers.

7 Wood-Burning and Pellet Stoves.

Gas Boilers (Including Combi Boilers).

8 Heat Pumps.

Fireplaces

9 Fireplaces

Wood-Burning and Pellet Stoves.

0 Heat Pumps.

Electric Heaters.

For the two options that have been assigned to you and assuming an 80 m2 house in Newcastle:

a) Identify the physical process of the two heating systems in terms of thermodynamics, heat

production, energy losses, heat transport, etc.

b) Evaluate the energy output and performance of the two systems.

c) Compare installation cost, carbon footprint, and annual cost of electricity or fuel charges.

d) Select the option with the lowest cost assuming a 12-year life span.

e) Choose the optimal option if fuel or electricity prices double to what they are now.

f) Propose solutions and/or improvements that might solve some of the problems of one of the

systems.

Use computational software such as MS Excel or MATLAB to perform the required calculations and

analyse the data.

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Marking Rubric

Systems Description (30%): The complexity of the two heat systems was investigated. A number of physical processes have been identified and addressed in

terms of thermodynamics, heat generation, heat losses, etc.

Excellent identification of the

physical processes involved in

the systems. Excellent

explanation supplemented by

well-represented figures

indicating a greater

understanding of the two

systems.

Good identification of the

physical processes involved in

the systems. Good explanation

supplemented by represented

figures indicating a good

understanding of the two

systems.

Adequate identification of the

physical processes involved in

the systems. Adequate

explanation supplemented by

some figures indicating a

sufficient understanding of the

two systems.

Limited identification of the

physical processes involved in the

systems. Some explanations are

supplemented by a few figures

indicating some understanding of

the two systems.

Little or incorrect identification of

the physical processes involved

in the systems. Little explanation

supplemented by few or no

figures indicating little

understanding of the two

systems.

High 1st Mid 1st Low 1st High 2:1 Mid 2:1 Low 2:1 High 2:2 Mid 2:2 Low 2:2 High Third Mid Third Low Third Close Fail Fail Poor Fail

(27-30) (24) (21) (20) (19) (18) (17) (16) (15) (14) (13) (12) (9) (6) (0-3)

System Analysis (30 %): The findings are analysed by making use of calculations. The results are well presented in graphs, tables, etc.

Excellent energy performance

analysis with a clear

understanding of processes and

using the system’s critical data.

The data are accurately

calculated and well presented.

Good analysis of energy

performance with some

understanding and using some

critical data of the system. The

data are accurately calculated

and well presented.

Adequate energy performance

analysis with some

understanding and using some

system data. The data are

calculated with some errors

and are well presented.

Limited energy performance

analysis with limited understanding

and using some system data. The

data are calculated with some

errors and inadequately presented.

Little analysis of the energy

performance with limited

understanding and little or no

system data. The data are

calculated with errors and

presented incorrectly.

High 1st Mid 1st Low 1st High 2:1 Mid 2:1 Low 2:1 High 2:2 Mid 2:2 Low 2:2 High Third Mid Third Low Third Close Fail Fail Poor Fail

(27-30) (24) (21) (20) (19) (18) (17) (16) (15) (14) (13) (12) (9) (6) (0-3)

SEPM | Learning and Teaching | Page 4 of 6

Discussions (20%): The obtained data are compared between the two systems. The disadvantage and advantages of each system are discussed. Conclusions

have been drawn for the selection of the best system based on the data

An excellent comparison of the

two systems with a clear

understanding of the advantages

and disadvantages of each

process. Excellent conclusion

demonstrating an outstanding

ability to justify their choice

A good comparison of the two

systems with a clear

understanding of the advantages

and disadvantages of each

process. Reasonable conclusion

demonstrating an excellent

ability to justify their choice

Adequate comparison of the two

systems with some

understanding of the

advantages and disadvantages

of each process. Sufficient

conclusion demonstrating some

ability to justify their choice.

Limited comparison of the two

systems with little

understanding of the

advantages and disadvantages

of each process. Acceptable

conclusions with limited

justification of their choice.

A little comparison of the two

systems with limited

understanding of the

advantages and disadvantages

of each process. The

conclusion does not justify the

choice of the heating system.

High 1st Mid 1st Low 1st High 2:1 Low 2:1 High 2:2 Low 2:2 High Third Low Third Close Fail Fail Poor Fail

(18-20) (16) (14) (13) (12) (11) (10) (9) (8) (6) (4) (0-2)

Performance Improvement (10%): The issues have been identified, and improvements have been suggested. The system improvement is shown through

mathematical analysis.

Excellently identification of the

energy performance issues with

the proposal of innovative

solutions proven through

thorough quantification.

Good identification of the

energy performance issues

with the proposal of good

solutions proven through

thorough quantification

Adequately identification of the

energy performance issues with

the proposal of innovative

solutions proven through some

quantification.

Limited identification of the

energy performance issues with

the proposal of innovative

solutions proven through limited

quantification

Poor identification of the energy

performance issues with the

proposal of innovative solutions

proven through no quantification

High 1st Mid 1st Low 1st 2:1 2:2 Third Close Fail Fail Poor Fail

(9-10) (8) (7) (6) (5) (4) (3) (2) (0-1)

Presentation/References (10%): Follow the poster template and structure, including section heading, table and figure format, caption, and references. Clear

figures are required, and all text and tables must be legible. Proper grammar, spelling, and engineering and mathematical symbols are required.

Excellent, well-structured, and

coherent presentation. An

excellent use of figures and

tables to highlight data. Excellent

presentation with no spelling or

grammatical errors

Well-structured and coherent

presentation. An excellent use

of figures and tables to highlight

data. Good presentation with

minimal spelling or grammatical

errors.

Adequately structured and

coherent presentation. A good

use of figures and tables to

highlight data. Average

presentation with some spelling

or grammatical errors.

Adequately structured and

coherent presentation. Limited

use of figures and tables to

highlight data. Below average

presentation. Numerous spelling

or grammatical errors

No clear structure to the

presentation. Figures and tables

do not highlight key results. Poor

or inadequate presentation

lacking the most basic writing

skills

High 1st Mid 1st Low 1st 2:1 2:2 Third Close Fail Fail Poor Fail

(9-10) (8) (7) (6) (5) (4) (3) (2) (0-1)

School of Engineering, Physics & Mathematics

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Expected Size of Submission

You will submit an A0-sized poster presentation in PowerPoint format. The PowerPoint presentation must

include calculations, diagrams, figures, tables, and text. All together (figures, calculations, tables and so on)

is equivalent to 2500 words.

Referencing Style

You must write your coursework using the Cite Them Right version of the Harvard referencing system. An

online guide to Cite Them Right is freely available to Northumbria University students at

Referral

The Referral Attempt opportunity will generally take place after the end-of-level Progression and Awards

Board (PAB). If you become eligible to complete a Referral Attempt but are subsequently unable to

undertake the opportunity when required, you will be permitted to re-sit the module at the next scheduled

sitting of the module assessment. This will typically entail the suspension of your progression on your

programme of study until such time that you have completed the level and become eligible to proceed.

Guidance for Students on Policies for Assessment

For full assessment regulations, feedback policies, and procedures (including late submission,

extensions, extenuating circumstances, and academic misconduct), see:

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Rubric

In the image

On the image

Control Systems (ELEC 20005.3)

Case Study Assignment

Lead-Lag Compensator Design for Robot Arm Positioning

The primary objectives are to:

Analyze the initial system performance

Design a lead compensator to improve transient response by reducing overshoot and settling time by 50%

Design a lag compensator to ensure steady-state error for ramp input is less than or equal to 0.1

Validate the design through simulations

Compare uncompensated and compensated system performance

Question 1.

A particle of mass m is attached to the rim of a disc of radius R that is pinned at its centre as shown. One end of a spring of stiffness k is attached to a drum of radius r that is itself rigidly attached to the disc. Knowing that the spring is unstretched when = 0, use the principle of virtual work to determine the value(s) of at equilibrium.

Question 2.

A ring of mass m slides without friction along a rod that is fixed at an angle with respect to the horizontal. A particle of mass M is attached to the ring with an inextensible, massless string of length L. Write Lagrange’s equations for the system.

Problem 1

Consider the system described by the following transfer function:

begin{equation*}

frac{Y(s)}{R(s)} = frac{6}{(s+1)^2(s+4)}.

end{equation*}

begin{enumerate}[label=alph*.]

item textbf{Find the differential equation of the system.}

item textbf{Derive a state-space representation of the system.}

end{enumerate}

Problem 2

For the system represented by:

[

A = begin{bmatrix} -3 & 1 \ -1 & 2 end{bmatrix},quad

B= begin{bmatrix}1\1end{bmatrix},quad

C= begin{bmatrix}1 & 0end{bmatrix},quad D=[0]

]

Find the transfer function of the system.

Problem 3

Consider the following block diagram representation of a feedback control system as shown in Figure~ref{fig:prob3}:

begin{enumerate}[label=alph*.]

item textbf{Determine the transfer function of the system.}

item textbf{Derive the state-space representation, $K=5$, $alpha=0.5$.}

item textbf{Confirm the transfer function using the state-space approach.}

end{enumerate}

Problem 4

Consider the system described by:

begin{equation*}

ddot{y} + dot{y} + tfrac{1}{2}y = tfrac{1}{2}u

end{equation*}

begin{enumerate}[label=alph*.]

item textbf{Find the state transition matrix $Phi(t)$.}

item textbf{Find the homogeneous state vector.}

item textbf{Find the homogeneous output response.}

item textbf{Find the transfer function of the system.}

item textbf{Find the forced state vector.}

item textbf{Find the forced output response.}

item textbf{Find the complete output response of the system.}

end{enumerate}

Problem 5

Consider the following electrical circuit shown in Figure~2:

begin{enumerate}[label=alph*.]

item textbf{Obtain the transfer function $E_o(s)/E_i(s)$.}

item textbf{Find the state-space representation of the system (i.e., the matrices A, B, C, and D).}

end{enumerate}

Problem 6

Consider the following state-space system:

[

dot{x}(t) =

begin{bmatrix}

-1 & 0 & 1 \

1 & -2 & 0 \

0 & 0 & -3

end{bmatrix}

x(t)

+

begin{bmatrix}

0 \

0 \

1

end{bmatrix}

u(t),

quad

y(t) =

begin{bmatrix}

1 & 1 & 0

end{bmatrix}

x(t).

]

subsection*{(a) State transition matrix $Phi(t)$}

subsection*{(b) Homogeneous output response with $x(0) = [1 ;; 0 ;; 1]^T$}

subsection*{(c) Transfer function}

Problem 7

Verify if the following matrices can be state transition matrices $Phi(t)$.

subsection*{(a)}

[

Phi(t) =

begin{bmatrix}

-e^{-t} & 0 \

0 & 1-e^{-t}

end{bmatrix}.

]

subsection*{(b)}

[

Phi(t) =

begin{bmatrix}

1-e^{-t} & 0 \

1 & e^{-t}

end{bmatrix}.

]

subsection*{(c)}

[

Phi(t) =

begin{bmatrix}

1 & 0 \

1-e^{-t} & e^{-t}

end{bmatrix}.

]

subsection*{(d)}

[

Phi(t) =

begin{bmatrix}

e^{-2t} & te^{-2t} & tfrac{t^2}{2}e^{-2t} \

0 & e^{-2t} & te^{-2t} \

0 & 0 & e^{-2t}

end{bmatrix}.

]

Problem 8

Consider the system with the following closed-loop transfer function:

[

frac{Y(s)}{R(s)} = frac{K}{0.5s^5+7s^4+3s^3+42s^2 + 4s +56}.

]

begin{enumerate}[label=alph*.]

item textbf{Apply the Routh-Hurwitz criterion to study the stability of the system.}

item textbf{Is there any pole on the $jomega$-axis? If yes, find them.}

end{enumerate}

Problem 9

Determine whether the system is stable or not, and if not, determine the number of unstable poles for the following characteristic equations:

begin{enumerate}[label=alph*.]

item $C.E_1 = s^5+2s^4+2s^3+2s^2+3s+4$

item $C.E_2 = s^6+s^5+3s^4+4s^3+s^2+s+1$

end{enumerate}

# HW1 Part 2 DNN Roofline & Benchmarking (Final, corrected)

#

# Run the notebook cells sequentially in Jupyter/Colab.

# Requirements: `torch`, `torchvision`, `timm`. Optional but recommended: `thop`, `ptflops`.

# This notebook implements Parts 15 and includes brief discussion Markdown blocks after each part.

#

# %%

# Cell 1: imports & device detection

# %%

# %% [markdown]

# ## Part 1 Chip analysis & roofline plot

# We collect peak FLOPs and memory bandwidth for a set of diverse chips including CPU, GPU, ASIC, and SoC.

# Replace numeric values with exact datasheet numbers + citations in your report.

# %%

# Cell 2: CHIP TABLE (datasheet example numbers)

# %%

# Cell 3: Roofline plotting helper

# %%

# %% [markdown]

# **Discussion Chip Analysis**

#

# In this comparison, high-end data center GPUs such as the NVIDIA A100 achieve the highest peak FLOPs and memory bandwidth, making them ideal for compute-intensive DNN workloads. ASICs such as Google TPU v3 are optimized for matrix operations and deliver excellent energy efficiency but are less flexible than GPUs. General-purpose CPUs (e.g., Intel Xeon Platinum 8380) have the lowest FLOPs but provide better versatility for mixed workloads. Mobile/SoC devices like Apple M2 and NVIDIA Jetson Orin NX demonstrate significantly lower absolute performance but much higher energy efficiency per watt, suitable for edge inference. Overall, GPUs dominate in raw performance, while ASICs and SoCs optimize for specialized efficiency.

# %%

# Cell 4: Models to analyze (>=6)

# %%

# %% [markdown]

# ## Part 2 DNN Compute & Memory Analysis

# For each selected model, compute FLOPs, parameter count, activation bytes, and operational intensity.

# FLOPs are computed using `thop` (MACsFLOPs) if available; otherwise `ptflops` is used (converted as needed).

# %%

# Cell 5: FLOPs & params computation (thop preferred) and activation memory estimation

# %%

# %% [markdown]

# **Discussion DNN Compute and Memory Analysis**

#

# The FLOPs and parameter counts vary widely across models.

# Lightweight models such as MobileNet V2 and EfficientNet-B0 show low FLOPs and memory footprints, resulting in higher operational intensity and better suitability for mobile/edge devices.

# In contrast, deeper networks like ResNet-50 and ViT-Base exhibit much higher FLOPs but also higher memory demands, making them more compute-bound.

# When overlaid on the GPU roofline, lightweight models tend to be **memory-bound**, while heavy architectures approach the **compute-bound** region of the curve.

# %%

# Cell 6: Visualize GFLOPs, Params, and Operational Intensity

# %%

# Cell 7: Overlay model operational intensities on the primary chip roofline

# %%

# %% [markdown]

# **Discussion DNN Compute/Memory Overlay**

#

# Overlaying operational intensity on the GPU roofline shows which models are memory-bound versus compute-bound on the primary GPU. Models with low operational intensity (small FLOPs per byte) land in the bandwidth-limited regime; models with large FLOPs per byte approach the compute limit.

# %%

# Cell 8: Benchmarking helpers and run benchmarks at batch sizes {1,64,128,256}

# %%

# %% [markdown]

# **Discussion Benchmarking**

#

# Latency generally increases with model complexity but is not perfectly correlated with FLOPs or parameter count. For convolutional models, FLOPs often predict latency better; transformer-based models may exhibit memory- and scheduling-induced deviations. Throughput scales with batch size until memory or hardware saturation limits further gains.

# %%

# Cell 9: Latency vs FLOPs and Latency vs Params (annotated)

# %%

# %% [markdown]

# **Discussion FLOPs vs Parameters as latency predictors**

#

# FLOPs sometimes align with latency for compute-bound workloads, but memory access patterns, kernel implementations, and batching affect runtime. Parameter count correlates imperfectly: high parameter models can be memory-hungry but not necessarily slow if compute is optimized. Use both metrics and measured latency to draw conclusions.

# %%

# Cell 10: Throughput vs batch (use base=2 for log scale)

# %%

# Cell 11: Measured performance overlay on roofline (compute FLOPs/sec from measured latencies)

# %%

# %% [markdown]

# **Discussion Hardware Utilization and Peak Performance**

#

# The measured GPU performance points are typically below the theoretical roofline. This gap arises from kernel launch overhead, memory access inefficiencies, and less-than-ideal use of specialized units (e.g., tensor cores). Models with low operational intensity are limited by memory bandwidth; compute-heavy models may approach the FLOP ceiling but still show headroom lost to implementation inefficiencies.

# %%

# Cell 12: Forward vs Backward runtime (runtime measured) and FLOPs heuristic

# %%

# %% [markdown]

# **Discussion Inference vs Training**

#

# The backward pass consistently takes roughly 23 the time of the forward pass, matching the expected FLOPs ratio. This occurs because gradients must be computed and stored for every parameter during training. Deeper models like ResNet-50 often show larger backward-to-forward ratios due to more operations and memory traffic. Pie charts (below) break down latency/FLOPs/activation by layer type for the forward pass and estimate the backward breakdown.

# %%

# Cell 13: Per-layer breakdown (resnet50 example) + forward/backward pie charts

# %%

# %% [markdown]

# **Discussion Per-layer breakdown and forward/backward**

#

# Convolutional and linear layers dominate both FLOPs and latency. The backward pass is estimated as ~2 forward FLOPs, and backward latency is distributed proportionally to forward-layer time when measured backward time is available. This provides an actionable decomposition for optimization.

# %%

# Cell 14: final notes + saved files

# Part 1a Chip Roofline Plot

# Define chip specs and plot_roofline function

# Part 2c Operational Intensity Overlay

# Collect model results into a list of dicts

# (Replace the FLOPs and memory values with the ones you already computed in Part 2a/2b)

# ————————————————-

# Part 2a & 2b Model FLOPs, Parameters, and Memory Footprint

# ————————————————-

# Pick the best device available (GPU MPS CPU)

# Example input for profiling (batch size 1, 3 channels, 224×224 image)

# Define the models you want to analyze

# Collect results

# —— Bar Chart: FLOPs ——

# —— Bar Chart: Parameters ——

# —— Bar Chart: Memory Footprint ——

Attached Files (PDF/DOCX): Assessment Brief.pdf

Note: Content extraction from these files is restricted, please review them manually.