Welcome to the AS 2 Practical Investigation Guide
Hello and welcome! In AS 2: Internal Assessment – Renewable Energy Technologies, you step into the shoes of an environmental technologist and renewable energy engineer. Rather than just reading about green energy, you will design, carry out, analyze, and evaluate your very own practical investigation.
Whether you are testing how blade pitch affects a micro-wind turbine, how light angle influences a solar photovoltaic (PV) panel, or how insulation alters the heat retention of a solar thermal collector, this guide breaks down every single stage into manageable steps. Don't worry if experimental coursework feels overwhelming at first—by following this step-by-step framework, you will be able to construct a top-tier scientific report!
Stage 1: Planning, Aims, and Hypotheses
Every great scientific breakthrough starts with a clear question. In your AS 2 investigation, you must establish exactly what you are testing and why.
1. Defining Aims and Background Theory
Your Aim is a straightforward statement outlining what you intend to investigate. Your background research should link directly to core renewable energy principles.
Example Aim: "To investigate how changing the angle of incidence of incoming light affects the electrical power output of a monocrystalline silicon solar PV cell."
2. Formulating a Scientific Hypothesis
A Hypothesis is a testable prediction supported by scientific reasoning. It should clearly state what you expect to happen and why.
Example Hypothesis: "As the angle of incidence decreases towards \(0^\circ\) (perpendicular to the panel surface), the power output of the PV cell will increase because light intensity per unit area is maximized."
3. Mastering Variables
To make your test scientifically valid and fair, you must manage three types of variables:
• Independent Variable: The factor you deliberately change (e.g., the tilt angle of a solar panel or the number of turbine blades). Memory trick: "I change the Independent variable."
• Dependent Variable: The factor you measure to get your results (e.g., voltage, current, power output, or temperature change). Memory trick: The Data you collect is the Dependent variable.
• Controlled Variables: All other factors that must be kept strictly constant throughout the experiment to ensure a fair test (e.g., distance from light source, ambient room temperature, or wind speed from a fan).
Did You Know? If you fail to keep even one controlled variable constant—such as room temperature during a solar PV test—it can skew your electrical resistance readings, making your data unreliable!
Key Takeaway: A strong plan clearly states the aim, provides a scientifically justified hypothesis, and explicitly identifies the independent, dependent, and all controlled variables.
Stage 2: Health, Safety, and Risk Assessment
Safety is paramount in any engineering laboratory. Examiners award credit for thorough, realistic risk assessments that go beyond generic statements.
The Three Elements of Risk Assessment
For every potential hazard in your renewable technology setup, you must document:
1. The Hazard: The item or situation that could cause harm (e.g., high-intensity halogen lamps, rotating wind turbine blades, hot water in solar thermal loops, or exposed electrical terminals).
2. The Risk: What could go wrong and the resulting injury (e.g., skin burns from touching a hot lamp casing, eye damage from intense light, or electric shock from loose wiring).
3. The Control Measure: The specific action taken to minimize or eliminate the risk (e.g., allowing lamps to cool before handling, using protective heat-resistant gloves, wearing safety goggles, and ensuring all circuit connections are insulated and checked before switching on power supplies).
Quick Review: Always remember the safety triad: Hazard \(\rightarrow\) Risk \(\rightarrow\) Control Measure.
Stage 3: Apparatus Selection, Setup, and Methodology
A well-planned method ensures that any other scientist could read your report, repeat your exact setup, and obtain comparable results.
1. Choosing the Right Instruments
Selecting appropriate equipment with suitable resolution (the smallest change the instrument can detect) is essential:
• Digital Multimeter: Used to measure current (\(I\)) in amperes (\(\text{A}\)) or milliamperes (\(\text{mA}\)), and potential difference (\(V\)) in volts (\(\text{V}\)).
• Anemometer: Measures wind speed in meters per second (\(\text{m/s}\)) for wind turbine testing.
• Lux Meter / Solar Power Meter: Measures light illuminance in lux or solar irradiance in watts per square meter (\(\text{W/m}^2\)).
• Digital Thermometer / Thermocouple: Measures temperature changes (\(\Delta T\)) in degrees Celsius (\(^\circ\text{C}\)).
• Variable Resistor / Decade Box: Allows you to vary electrical load to determine maximum power point (MPP).
2. Step-by-Step Method and Reliability
To ensure high reliability:
• Repeat Readings: Take at least three separate readings for each value of the independent variable.
• Calculate Means: Average your repeat readings after discarding any obvious anomalous results.
• Adequate Range: Test at least 5 to 6 different values across a broad, sensible range (e.g., angles from \(0^\circ\) to \(90^\circ\) in steps of \(15^\circ\)).
Key Takeaway: High-quality data depends on choosing instruments with suitable precision and resolution, and taking repeat readings across a wide test range.
Stage 4: Data Collection, Processing, and Calculations
Once your raw data is recorded in a clear table with full column headings and units, you must process that data using relevant renewable energy equations.
Essential Formulas for AS 2
1. Electrical Power Output (\(P\))
For solar PV cells and wind turbines generating electricity:
\(P = V \times I\)
Where:
• \(P\) = Power in watts (\(\text{W}\))
• \(V\) = Potential difference in volts (\(\text{V}\))
• \(I\) = Current in amperes (\(\text{A}\))
2. Thermal Energy Transfer (\(Q\))
For solar thermal collectors and heat pump systems heating water:
\(Q = m \times c \times \Delta T\)
Where:
• \(Q\) = Thermal energy transferred in joules (\(\text{J}\))
• \(m\) = Mass of water in kilograms (\(\text{kg}\))
• \(c\) = Specific heat capacity of water (\(4186\text{ J/kg}^\circ\text{C}\) or approx. \(4200\text{ J/kg}^\circ\text{C}\))
• \(\Delta T\) = Temperature change in degrees Celsius (\(^\circ\text{C}\))
3. Percentage Efficiency
To determine how effectively your system converts input energy into useful output:
\(\text{Efficiency } (\%) = \left( \frac{\text{Useful Energy or Power Output}}{\text{Total Energy or Power Input}} \right) \times 100\)
Presenting Graphs Correctly
When plotting your processed data:
• Place the independent variable on the horizontal \(x\)-axis.
• Place the dependent variable on the vertical \(y\)-axis.
• Include clear axis labels with quantities and units, e.g., "Blade Angle (\(^\circ\))" or "Power Output (\(\text{W}\))".
• Choose sensible, linear scales that occupy more than \(50\%\) of the graph grid.
• Draw a smooth line of best fit (or curve of best fit) that represents the overall trend, ignoring outliers.
Key Takeaway: Always show full working out for calculations, include correct units at every step, and plot clear graphs with well-scaled axes and appropriate lines of best fit.
Stage 5: Analysis, Conclusion, and Evaluation
The final phase of your investigation is where you demonstrate critical thinking by interpreting your findings and appraising your experimental technique.
1. Drawing Scientific Conclusions
• State the clear relationship shown by your results (e.g., "As blade pitch angle increased from \(10^\circ\) to \(30^\circ\), electrical power increased to a peak of \(1.45\text{ W}\), before decreasing at steeper angles").
• Explain the scientific reason behind this outcome using underlying environmental technology theory.
• Directly refer back to your original hypothesis—state clearly whether your experimental evidence supports or refutes it.
2. Identifying Experimental Errors
Understanding where errors come from shows strong analytical skills:
• Random Errors: Unpredictable fluctuations that cause individual readings to differ from the true value (e.g., slight drafts altering wind turbine speed, or fluctuating ambient sunlight during testing). These are reduced by taking repeated trials and calculating averages.
• Systematic Errors: Constant shifts in readings caused by flawed equipment or calibration (e.g., a multimeter that reads \(+0.05\text{ V}\) when no voltage is applied—known as a zero error). These cannot be fixed by averaging; equipment must be recalibrated or zeroed.
3. Evaluating Limitations and Proposing Improvements
Avoid vague statements like "I would be more careful next time." Instead, provide realistic, technically specific improvements:
Instead of: "The lamp was inconsistent."
Say: "The halogen light source radiated excess heat over time, raising the PV panel temperature and lowering its efficiency. In future trials, a cooling heat sink or a constant-temperature LED solar simulator should be utilized."
Instead of: "The wind was uneven."
Say: "Turbulence from the desk fan created uneven airflow across the blades. Using a flow-straightening wind tunnel with a digital honeycomb anemometer would ensure uniform laminar wind velocity."
Key Takeaway: A top-grade evaluation distinguishes between random and systematic errors, critiques specific weaknesses in the setup, and provides concrete, realistic engineering solutions for future testing.
Summary Checklist for AS 2 Practical Success
Before submitting your practical investigation portfolio, ensure you have ticked off each of the following:
• Clear Context & Aim: Well-researched background theory and a focused objective.
• Detailed Hypothesis: A testable statement backed by scientific principles.
• Identified Variables: Independent, dependent, and all control variables explicitly listed.
• Comprehensive Risk Assessment: Hazards, risks, and control measures clearly tabulated.
• Robust Methodology: Repeat measurements, wide test ranges, and suitable measuring instruments.
• Accurate Data Processing: Formulas applied correctly with full working, units, and clear graphs.
• Critical Evaluation: Evidence-based conclusions, analysis of errors, and detailed technical improvements.