Testing and analysing designs
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Lesson notes
The Frozen Pipes Problem
- The engineering task: a cabin in Utah's Uinta Mountains gets very cold in winter, and if it is left without heat, the water pipes inside can freeze and burst.
- The challenge is to design, construct, and test a device that affects the rate at which the water in the pipes freezes.
- Before designing a solution, engineers identify the criteria (the positive outcomes they want) and the constraints (the limits they must design within).
- This is an engineering design task: the goal is not just to build one device, but to test several competing designs, compare their data, and combine the best features of each into an improved solution.
A cabin in Utah's Uinta Mountains, whose water pipes may freeze in winter
Criteria and Constraints
- Criteria are the positive outcomes a design should achieve — for the frozen-pipes problem, keeping the water from freezing for as long as possible is a criterion.
- Constraints are the limits a design must fit within, such as a budget, a maximum size or weight, or how easy the materials are to work with.
- Before comparing designs, engineers list the research questions this raises: what materials are available, what do they cost, and what limits apply to the finished device?
- A design that meets every criterion but breaks a constraint — for example, costs far more than the budget allows — is not an acceptable solution.
Researching and Developing Possible Designs
- Once the criteria and constraints are clear, engineers research possible solutions — for example, comparing different materials that might slow the freezing of the pipes.
- Developing possible designs takes imagination as well as reasoning based on what the research found.
- Most problems have more than one reasonable design — this is why testing and comparing competing designs matters.
- Each candidate design is a hypothesis about what will work; only testing can show whether it actually meets the criteria.
Building and Testing a Model
- A model of each design is built and tested before a final version is produced.
- Testing is what allows problems with a design to be found and fixed early, rather than after the device is finished.
- Every design should be tested under the same conditions so the results can be fairly compared — for example, the same starting temperature and the same length of time.
- The result of a test is data: a measurement, such as how long a pipe stayed above freezing, that can be compared across designs.
Analysing Test Data to Compare Designs
- When several designs are tested, the next step is to analyse the data to find the similarities and differences between them.
- Comparing data might show that one design kept water warm for longer, another was cheaper, and a third was easiest to install.
- Analysing the data identifies the best characteristics of each competing design — not just which design is best overall.
- A conclusion drawn from test data should be based only on what the data actually shows, not on which design looks the most impressive.
Combining the Best Features into a New Solution
- The engineering design process does not stop at picking a single winning design — the best characteristics of each competing device can be combined into a new solution.
- For example, if one design's material was the most effective at slowing freezing and a different design's shape was the cheapest to build, a new design could combine both features.
- Combining features usually produces a solution that meets the criteria and constraints better than any one of the original designs did alone.
- This is the heart of the engineering design standard for this task: compare, identify the best of each, and combine.
Modifying and Retesting
- After testing and analysing the data, a design is usually modified to fix problems the test revealed or to add a strength borrowed from another design.
- The modified design is then retested to check whether the change actually improved performance.
- This test-analyse-modify cycle repeats until a design meets its criteria while staying within its constraints.
- Only once a design passes this cycle is it shared as a finished solution to the problem.
Slides
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Practice questions
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1.What is the purpose of testing and analyzing designs in engineering?
Easy- ATo identify the best features and combine them into an improved design
- BTo prove that the first design is always the best
- CTo make the design more expensive
- DTo avoid making any changes to the design
2.In an experiment, what is the variable that is changed by the experimenter called?
Easy- AIndependent variable
- BDependent variable
- CControl variable
- DConstant variable
3.Which of the following is an example of a dependent variable in an experiment testing the strength of different bridge designs?
Easy- AThe maximum load the bridge can hold
- BThe type of material used
- CThe length of the bridge
- DThe shape of the bridge
4.Why is it important to have a control group in an experiment?
Medium- ATo provide a baseline for comparison
- BTo make the experiment more complicated
- CTo ensure all variables are changed
- DTo eliminate the need for replication
5.What does it mean if an experiment is 'replicable'?
Medium- AOther scientists can repeat the experiment and get similar results
- BThe experiment can only be done once
- CThe results are always the same no matter what
- DThe experiment is very expensive
6.In a test of a new solar panel design, what is a control variable?
Medium- AThe angle of the sun
- BThe amount of electricity generated
- CThe type of solar panel
- DThe temperature of the panel
7.A student tests three different paper airplane designs by throwing each one three times and measuring the distance flown. Which of the following is the best way to analyze the data?
Hard- ACalculate the average distance for each design and compare the averages
- BUse only the longest flight for each design
- CIgnore the results that are very different from the others
- DAdd all distances together and divide by the number of designs
8.What is the main advantage of random assignment in an experiment?
Hard- AIt helps reduce bias and confounding variables
- BIt guarantees the experiment will work
- CIt makes the experiment easier to conduct
- DIt ensures all participants are the same
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