Creating Q-vs-T Plots: Analyzing Energy Performance

Nov 4, 2025 by Admin 52 views

Hey there, data enthusiasts! Ever found yourself knee-deep in energy simulation results and wished for a clearer picture of what's happening? Well, Q-vs-T plots are your secret weapon! These plots, also known as Q-T plots, are fantastic for visualizing the relationship between energy transfer (Q, measured in kWh) and temperature (T, measured in °C). They're especially useful when analyzing the performance of thermal systems, allowing you to see how energy flows and transforms within the system. We're going to dive into how to generate these plots using Python and the SPF-OST and pytrnsys_process libraries. So, let's get started!

Unveiling the Power of Q-vs-T Plots

So, what's the big deal about Q-vs-T plots? Why bother with them? Think of it this way: you're trying to understand how a heat pump or solar collector is working. You have tons of data—temperatures, energy flows, etc. A Q-vs-T plot lets you see at a glance how much energy is being transferred at what temperature. This is super valuable for several reasons:

Performance Evaluation: You can quickly see if the system is performing as expected. Are there any unexpected energy losses or gains? Is the system operating efficiently across different temperature ranges?
Component Analysis: You can analyze the performance of individual components like heat exchangers, evaporators, and condensers. How does the energy transfer in each component change with temperature?
Optimization: Q-vs-T plots can guide you in optimizing system performance. Maybe a particular component needs tweaking, or the system could benefit from a different operating strategy.
Troubleshooting: If something's not right, a Q-vs-T plot can help you pinpoint the issue. Is there a sudden drop in energy transfer at a specific temperature? That could indicate a problem.

Basically, these plots turn complex data into a simple, understandable visual. They help you see the forest for the trees! They are so helpful in understanding the heat exchange process. Also they could be an important tool for the heat exchanger design.

Python Code Breakdown: Crafting the Q-vs-T Plot

Alright, let's get into the nitty-gritty of generating these plots using Python and some awesome libraries. Here is the code block that you've given:

names_legend = [
    '$Q_{coll}(T_{in}){{content}}#39;,
    '$Q_{coll}(T_{out}){{content}}#39;,
    '$Q_{Hx}(T_{in}){{content}}#39;,
    '$Q_{Hx}(T_{out}){{content}}#39;,
    '$Q_{evap}(T_{in}){{content}}#39;,
    '$Q_{evap}(T_{out}){{content}}#39;,
    '$Q_{dem}(T_{in}){{content}}#39;,
    '$Q_{dem}(T_{out}){{content}}#39;,
]
plot_variables = [
    ["CollP_kW_calc", "CollTIn"],
    ["CollP_kW_calc", "CollTOut"],
    ["HxQ_kW", "HxTSide1In"],
    ["HxQ_kW", "HxTSide1Out"],
    ["HpQEvap_kW", "HpTEvapIn"],
    ["HpQEvap_kW", "HpTEvapOut"],
    ["QSnkP_kW", "QSnkTIn"],
    ["QSnkP_kW", "QSnkTOut"],
]

dataframes = pd.DataFrame() #[]

_plt.figure()

for q, t in plot_variables:
    a = abs(sim.hourly[q])
    b = sim.hourly[t]
    df = pd.DataFrame({q: a, t: b})

    df = df.sort_values(by=t)
    df[q] = np.cumsum(df[q])
    # dataframes = dataframes + a + b
    # dataframes.append(df)

    fig = _plt.plot(df[t], df[q], label=q + "____" + t)  # , linestyle='-', color='blue', label='Q_cum')
    
_plt.xlabel('Temperature [°C]')
_plt.ylabel('Cumulative energy [kWh]')
_plt.grid()
_plt.legend(names_legend)
api.export_plots_in_configured_formats(fig[0].figure, sim.path, "q_t", "q_vs_t")

Let's break it down piece by piece:

Libraries: The code uses pandas for data manipulation, matplotlib.pyplot (_plt) for plotting, and numpy for numerical operations. You'll also need the SPF-OST and pytrnsys_process libraries (presumably imported as sim and api respectively). Make sure you have these installed!
names_legend: This list defines the labels for each line in your plot. These labels clearly indicate which energy flow is being represented (e.g., $Q_{coll}(T_{in})$ for the collector's energy at the inlet temperature).
plot_variables: This is the heart of the code. It's a list of lists. Each inner list contains two elements: the name of the energy variable (Q, e.g., CollP_kW_calc) and the name of the corresponding temperature variable (T, e.g., CollTIn). These variable names are how your data is stored in your sim.hourly data structure. Make sure these variable names match your data exactly!
Data Preparation: The code iterates through the plot_variables. Inside the loop:
- It extracts the energy (Q) and temperature (T) data from sim.hourly. The abs() function is used to ensure that the energy values are positive, which is crucial for the cumulative sum calculation.
- A Pandas DataFrame is created to hold the Q and T values.
- The DataFrame is sorted by temperature (T).
- The crucial step: df[q] = np.cumsum(df[q]). This calculates the cumulative energy. This is what gives you that characteristic Q-vs-T plot shape. It shows the total energy transferred up to a given temperature.
Plotting: _plt.plot(df[t], df[q], label=q + "____" + t) plots the temperature (x-axis) against the cumulative energy (y-axis). The label argument is used to create the legend.
Labels and Formatting: The code adds labels to the axes (_plt.xlabel, _plt.ylabel), adds a grid for easier reading (_plt.grid()), and includes a legend (_plt.legend(names_legend)) to identify each line.
Exporting: Finally, the plot is exported using api.export_plots_in_configured_formats(). This likely saves the plot to a file in a format you specify (e.g., PNG, PDF).

Interpreting the Q-vs-T Plot

Okay, the code's running, and you've got a plot! Now what? Understanding how to interpret your Q-vs-T plot is crucial. Here's a guide:

X-axis (Temperature): The horizontal axis represents temperature, usually in degrees Celsius (°C).
Y-axis (Cumulative Energy): The vertical axis shows the cumulative energy transfer, often in kilowatt-hours (kWh).
The Curve: Each line on the plot represents a different energy flow or component within your system. The slope of the line tells you how the energy transfer changes with temperature.
- Steep Slope: A steep slope indicates a high rate of energy transfer. Large amounts of energy are being transferred over a small temperature change. This could be good (efficient heat transfer) or bad (high heat loss). Strong Example: A steep slope for the energy absorbed by a solar collector is a good sign!
- Flat Slope: A flat slope means there's little or no energy transfer happening at that temperature. The system might be shut off, or a component might be inactive.
- Changes in Slope: Look for changes in the slope. These can indicate phase changes (like boiling or condensation), the activation of a component, or a change in operating conditions.
Comparing Lines: Compare the lines for different components. Do they behave as expected? Are there any discrepancies that need investigation?

Key Takeaways: The area under the curve can also be significant and can represent the total energy transferred over the temperature range. Also by comparing the curves, you can identify where energy losses occur. Strong tip : Always pay attention to the labels. They help you understand which part of the system the curve is related to.

Refining the Plots for Clarity

Creating effective Q-vs-T plots is an iterative process. Here's how to make them even more informative:

Clear Labels: Ensure your axes are clearly labeled with units. The legend should be easy to read and understand. Consider using LaTeX for mathematical symbols (as in the example code) to improve readability.
Data Cleaning: Before plotting, clean your data. Remove any outliers or data points that might skew the results. Check for missing values and handle them appropriately.
Smoothing: If your data is noisy, consider smoothing the curves. You can use moving averages or other smoothing techniques to reduce the visual clutter.
Multiple Plots: If you have a lot of data, consider creating multiple plots. You might focus on specific temperature ranges or zoom in on the performance of individual components.
Color-Coding: Use color-coding effectively to differentiate between different energy flows or components. Choose colors that are easily distinguishable.
Adding Annotations: Add annotations to the plot to highlight important points or events. For example, you could mark the temperature at which a component turns on or off.
Contextualization: Always interpret your plots in the context of your system design and operating conditions. What were the weather conditions during the simulation? What are the design parameters of your components?

Strong tip: By following these tips, you'll be able to quickly gain valuable insights from your data!

Conclusion: Unlock the Energy Insights

And there you have it, folks! Q-vs-T plots are a powerful tool for analyzing the energy performance of thermal systems. By understanding how to generate these plots and interpret the results, you can gain a deeper understanding of your system's behavior, identify areas for improvement, and optimize your designs for maximum efficiency. Don't be afraid to experiment, explore your data, and see what insights you can uncover. Happy plotting, and may your energy systems always run efficiently! Remember to always keep in mind the main goal of this approach is to make analysis of complex systems easier to understand!