Source: ENGR 216 Lectures
Tags: engineering design process, EDP, bio-inspired design, sustainability, engineering ethics, uncertainty, error analysis, data acquisition, Python, lab report, systems thinking, ABET, ENGR 216
ENGR 216 weaves three non-physics threads through the semester: the engineering design process (a structured approach to solving open-ended problems), engineering ethics (recognising and reasoning through professional dilemmas), and laboratory/data skills (uncertainty analysis, Python data acquisition, and writing proper lab reports). These topics account for roughly half the lecture content and appear on both the mid-term and final exam.
Engineering design process (EDP)
A systematic, iterative framework for solving engineering problems. Typical stages: define the problem, research, brainstorm, select a solution, prototype, test, evaluate, and iterate. The process is non-linear; you revisit earlier stages as new information emerges.
Agile development
An iterative project management approach that breaks work into short cycles (sprints), with frequent reassessment and adaptation. Used in ENGR 216 lab projects to manage team progress.
Bio-inspired design (biomimicry)
Design that draws solutions from biological systems. Examples include Velcro (inspired by burdock burrs), shark-skin surfaces for drag reduction, and structural designs modelled on bone or coral.
Sustainability
Designing systems that meet present needs without compromising the ability of future generations to meet theirs. Evaluated across environmental, economic, and social dimensions.
ABET (Accreditation Board for Engineering and Technology)
The organisation that accredits engineering programmes. An ABET-accredited degree is required to sit for the Professional Engineer (PE) exam. ABET outcomes include problem-solving, design, communication, ethics, teamwork, experimentation, and lifelong learning.
Engineering ethics
The set of moral principles governing professional engineering practice. Includes duties to public safety, honesty, competence, and sustainability. Engineers are expected to identify ethical dilemmas and apply structured reasoning to address them.
Uncertainty (measurement error)
The range within which the true value of a measured quantity is expected to lie. No measurement is exact. Uncertainty is quantified and propagated through calculations.
Systematic error
A consistent, repeatable bias in measurements caused by equipment calibration, method, or environment. Always shifts results in the same direction.
Random error
Unpredictable fluctuations in measurements from trial to trial. Reduced by averaging multiple measurements. Characterised by standard deviation.
Standard deviation (σ)
A measure of the spread of repeated measurements around their mean: σ = √[Σ(x_i − x̄)² / (N − 1)]. Smaller σ means more precise data.
Propagation of uncertainty
Rules for calculating the uncertainty of a result from the uncertainties of its inputs. For addition/subtraction, add absolute uncertainties in quadrature. For multiplication/division, add relative (percentage) uncertainties in quadrature.
Data acquisition (DAQ)
The process of collecting physical measurements (voltage, force, position) using sensors and converting them to digital data via a computer interface. In ENGR 216, this uses Python scripts on a Linux lab computer.
Systems thinking
Analysing problems by considering how components, relationships, and feedback loops interact within a broader system, rather than isolating individual parts.
The EDP taught in ENGR 216 follows these stages:
Define the problem: Identify the need, constraints, and criteria for success. A well-defined problem is half solved.
Research: Gather information on existing solutions, relevant physics, available materials, and applicable standards.
Brainstorm: Generate multiple solution concepts without filtering. Quantity matters more than quality at this stage.
Select: Evaluate concepts against criteria (feasibility, cost, safety, performance) and choose the most promising one.
Prototype: Build a working model, even if rough.
Test: Collect data and compare performance to specifications.
Evaluate and iterate: Determine what worked, what failed, and refine. Return to earlier stages as needed.
The EDP is iterative, not a straight pipeline. Expect to loop back multiple times during the six lab projects.
Bio-inspired design is taught early in the course as an example of the "research" and "brainstorm" stages of the EDP. The core principle is that evolution has already optimised many structural and functional solutions over millions of years.
Sustainability connects to ethics and design: an engineer's choices about materials, energy use, manufacturing processes, and end-of-life disposal have long-term consequences. Expect exam questions that ask you to identify sustainability considerations in a given design scenario.
Ethics content spans several lectures and an OOCL case study. The key frameworks:
Utilitarian analysis: Choose the action that produces the greatest good for the greatest number.
Duty-based (deontological) analysis: Some actions are right or wrong regardless of outcomes, based on obligations (honesty, promise-keeping, protecting the public).
Virtue ethics: Focus on the character of the decision-maker. What would a virtuous engineer do?
The NSPE (National Society of Professional Engineers) Code of Ethics provides specific guidance: hold paramount the safety, health, and welfare of the public; perform services only in areas of competence; issue public statements only in an objective and truthful manner.
Case studies (e.g. Challenger disaster, Hyatt Regency walkway collapse, Therac-25) are commonly used to illustrate ethical failures and the engineer's role in preventing harm.
Every lab report requires uncertainty analysis. The basics:
Expressing uncertainty: A measurement is reported as x ± δx, where δx is the absolute uncertainty. Example: L = 1.52 ± 0.01 m.
Absolute vs. relative uncertainty:
Absolute: δx (same units as x)
Relative: δx/x (dimensionless, often expressed as a percentage)
Propagation rules (independent, random uncertainties):
For q = a + b or q = a − b: δq = √(δa² + δb²)
For q = a × b or q = a / b: δq/|q| = √((δa/a)² + (δb/b)²)
For q = aⁿ: δq/|q| = |n| × (δa/a)
Comparing measured and theoretical values: Percent error = |measured − theoretical| / theoretical × 100%
Agreement is assessed by checking whether the theoretical value falls within the uncertainty range of the measured value.
ENGR 216 builds on Python skills from ENGR 102. Expect to:
Write or modify Python scripts that read sensor data from lab equipment.
Use libraries like matplotlib for plotting and numpy for calculations.
Generate graphs with proper axis labels, units, titles, and error bars.
Perform linear regression (line of best fit) and extract slope/intercept.
Use Excel or Python (PyGraphs) for OOCL assignments involving data visualisation.
Each lab project produces a team report. Standard structure:
Title page: Lab title, team members, date.
Introduction/Theory: The physics behind the experiment, relevant equations, and what the lab aims to verify.
Procedure: What you did, described so someone could reproduce it.
Data/Results: Tables of raw data, calculated results, graphs, and uncertainty analysis.
Discussion: Interpretation of results, comparison to theory, sources of error, and what you learned.
Conclusion: Brief summary of findings and whether the objectives were met.
Reports are due 8 days after the lab. Late penalty is 0.5% per hour, with a hard cutoff at 3 days late.
Standard deviation: σ = √[Σ(x_i − x̄)² / (N − 1)]
Propagation (addition/subtraction): δq = √(δa² + δb²)
Propagation (multiplication/division): δq/|q| = √((δa/a)² + (δb/b)²)
Propagation (power): δq/|q| = |n| · (δa/a)
Percent error: % error = |measured − theoretical| / theoretical × 100%
⚠️ The EDP is tested conceptually. You may be given a scenario and asked to identify which stage of the process applies or what the next step should be.
⚠️ Ethics questions appear on the final exam. Expect a scenario where you must identify the ethical issue, the stakeholders, and apply at least one ethical framework (utilitarian, duty-based, or virtue) to reason through it.
⚠️ Uncertainty propagation is tested both in lab reports and on exams. Practise the quadrature rules until they are automatic.
⚠️ Know the difference between systematic and random error, and be able to give examples of each from your lab experience.
⚠️ ABET outcomes may appear as a framing question: "Which ABET outcome does this activity address?" Know the seven outcomes listed in the syllabus.
⚠️ The Industry Nights and Department Information Saturday essays (3% of your grade) must be your own words with personal reflection. Do not use AI tools for these.
Q: Name the stages of the engineering design process in order.
A: Define the problem, research, brainstorm, select a solution, prototype, test, evaluate, and iterate. The process is cyclical and revisits earlier stages.
Q: You measure a length as 2.50 ± 0.02 m and a width as 1.20 ± 0.01 m. What is the uncertainty in the area?
A: Area = 2.50 × 1.20 = 3.00 m². Relative uncertainty: √((0.02/2.50)² + (0.01/1.20)²) = √(0.0064% + 0.00694%) ≈ √(0.000064 + 0.0000694) = √(0.0001334) ≈ 0.01155. So δA ≈ 3.00 × 0.01155 ≈ 0.035 m². Report as 3.00 ± 0.04 m².
Q: What is the difference between systematic and random error?
A: Systematic error is a consistent bias that shifts all measurements in the same direction (e.g. a miscalibrated scale). Random error is unpredictable fluctuation that varies from measurement to measurement and averages out with more trials.
Q: An engineer discovers that a bridge design they approved has a safety flaw. What does the NSPE Code of Ethics require?
A: The engineer must hold paramount the safety, health, and welfare of the public. They should report the flaw to the appropriate authorities and take steps to prevent harm, even if it conflicts with the interests of their employer or client.
Q: What does ABET stand for, and why does accreditation matter?
A: Accreditation Board for Engineering and Technology. An engineering degree from an ABET-accredited programme is required to sit for the Professional Engineer (PE) licensing exam, which is legally required for offering engineering services to the public in most jurisdictions.
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