Interactive Thermodynamics Wiley
Custodio Groves
Interactive Thermodynamics is a branch of science that deals with the study of energy and its transformations, particularly in relation to heat and work. It involves the use of interactive tools and simulations to understand and analyze the behavior of thermodynamic systems.
Interactive Thermodynamics differs from traditional thermodynamics in its use of interactive tools and simulations to visualize and analyze thermodynamic systems. This allows for a more intuitive understanding of complex concepts and phenomena.
Interactive Thermodynamics offers several benefits, including a better understanding of thermodynamics through visualization, the ability to simulate and analyze complex systems, and the potential for discovering new insights and applications.
Interactive Thermodynamics has a wide range of applications in fields such as engineering, chemistry, and physics. It is used to design and optimize energy systems, understand and predict the behavior of materials, and develop new technologies.
While some simulations and tools may require specific software or hardware, there are many interactive thermodynamics resources available online that can be accessed through a web browser. Some universities and research institutions also have specialized software and equipment for more advanced studies.
Example applet produced using code running in a Jupyter notebook. LCL denotes the lifting condensation level, z height, RH relative humidity, and wl the liquid water mixing ratio. The toggle buttons change the temperature (T) and dewpoint temperature (Tdew) at the surface.
Applet animating a collector droplet with radius R grazing on droplets with size r. The drops settle at terminal velocity υR and υr, respectively. The range sliders can be used to set the droplet radii, the horizontal distance between drop centers (y) and the vertical distance between drop centers (z). All units are in μm.
Active learning pedagogies are becoming more popular in higher education. Example short active learning activities include think-pair share exercises, brainstorming exercises, and minute papers. More formalized pedagogical approaches include flipped or blended classrooms (Baepler et al. 2014), problem-based learning, process-oriented guided inquiry learning (POGIL), and peer-led team learning (Eberlein et al. 2008). Active learning is student-centered. Collaborative teams work on specially designed activities. The instructor serves as facilitator while keeping formal lecture time to a minimum. Student-centered learning improves performance (Freeman et al. 2014; Soltis et al. 2015) and increases the engagement of underrepresented groups (Haak et al. 2011; Snyder et al. 2016).
Significant barriers prevent widespread adoption of student-centered pedagogies. Student-centered learning requires redesigned learning spaces, which are becoming available on many campuses but are not yet ubiquitous (Baepler et al. 2014). Few activity collections are available. For example, the POGIL project (www.pogil.org) lists POGIL-based textbooks in a variety of fields. These skew heavily toward chemistry and introductory courses. No texts are available for atmospheric science courses. Creating activities is time consuming for the instructor. Deslauriers et al. (2019) show that the increased cognitive effort associated with active learning leads to an incorrect student perception that they learn less. This can explain lower scores on student evaluations of teaching in active learning settings (Walker et al. 2008). All of the above conspire to limit adoption of student-centered approaches in environments where tenure and promotion criteria incentivize maximizing research productivity and numerical scores from student evaluations of teaching.
This work aims to lower the barrier to apply active learning in upper-level undergraduate and graduate courses. To this end, I introduce interactive worksheets for teaching about atmospheric aerosol and cloud physics. Applets embedded in the worksheets allow students to playfully interact with physical relationships and atmospheric models. Special emphasis is placed on incorporating real-world data and on practicing graph comprehension. The applets are built using emerging software technologies that allow scalable and reliable delivery of advanced content to any device with a web browser and Internet access. All content is free to be adopted, shared, and adapted. The framework is also suitable for archiving data and computing environments in compliance with new policies for scientific data management and stewardship.
The interactive worksheets are delivered as Jupyter notebooks running on a cloud server (Perkel 2018). A Jupyter notebook is served to the user in a web browser and is organized into cells. A cell can display content written in the markdown markup language, which is similar to and simpler than HTML. These cells can display formatted text, formatted equations, videos, images, tables, and hyperlinks. A cell can also execute computer code written in programming languages such as Julia, Python, R, and many more. Output of the program can be placed in a cell and can consist of text or graphics. Using reactive programming (Elliott and Hudak 1997; Czaplicki and Chong 2013) one can create content that allows users to interact with the program through intuitive web elements such as range sliders, toggle buttons, switches, and input boxes. Figure 1 shows an example applet illustrating the vertical profiles of temperature, relative humidity, and liquid water mixing ratio during closed-parcel adiabatic ascent of an air parcel. Toggle buttons can be used to change the surface temperature and dewpoint temperature. Upon a click, the three graphs update to the new initial conditions.
The underlying thermodynamic relationships that govern the profiles in Fig. 1 are typically taught in a sophomore-level course on atmospheric thermodynamics, which is an informal prerequisite for the atmospheric physics course taught by the author. The example is used as refresher activity. During lecture, the activity is briefly introduced. Additional background information is provided in a cell that precedes the applet. Students then work in groups to answer guided inquiry questions. They interact with the applet by changing inputs and with their peers to cooperatively answer the questions. Individualized instruction supports the learning process. Upon completion of the activity, the instructor solicits answers to the questions from each group, followed by a summary of key points that are relevant to complete subsequent activities. Three example key points derived from this activity are that the height of the lifting condensation level (LCL) is determined by the difference between surface temperature and dewpoint temperature, that the increase in RH with height is nonlinear far below the LCL and approximately linear near the LCL, and that the liquid water mixing ratio scales with the LCL temperature and the height above the LCL.
Overarching themes associated with the project content are the incorporation of real-world data and the focus on graph comprehension. Graph comprehension refers to the ability of students to derive meaning from graphs in a timely manner. Shah and Hoeffner (2002) reviewed the graph comprehension literature and made four recommendations. These are 1) to teach graph literacy skills in the context of science, 2) to translate representations to link visual features to the associated quantitative information, 3) to focus on the links between visual features and meaning, and 4) to train viewers to think of graph reading as an interpretation and evaluation task. Eye-tracking studies have shown that inexperienced viewers rely on cues in question prompts to complete the graph-based tasks, while experienced viewers focus on contextual elements such as graph titles, captions, and legends (Harsh et al. 2019). Frequent graphing assignments and a consistent graphical language help students to master graph comprehension (Shah and Hoeffner 2002; Violin and Forster 2018).
Formal design guidelines for graphical displays of quantitative information have been articulated by Kosslyn (1989), Wilkinson (1999), and Tufte (2001). The Grammar of Graphics introduced by Wilkinson (1999) inspired the plotting packages ggplot2 for the R language (Wickham 2016), and Gadfly for the Julia language (Jones et al. 2018). Graphical displays in the applets follow the Grammar of Graphics rules. By relying on a formal grammar, the contextual elements such as graph titles, construction of axis, and the use of a color key and/or a shape key are applied uniformly throughout. Furthermore, the Grammar of Graphics rules place graphical elements in a prescribed manner and restrict the creator by forbidding confusing constructs such as dual axes. This ensure that a consistent graphical language is used throughout and thus reduces the mental overhead associated with decoding graphs.
Figure 2 shows a typical example activity involving real-world data analysis while practicing graph comprehension. As suggested by Twomey (1959), cloud condensation nuclei (CCN) spectra are often suitably fit to a power law of the form NCCN = Csk, where C and k are empirical coefficients and s is the supersaturation in percent. Adjustment of the range sliders moves the fit line. Students are tasked to tabulate C and k for the eastern Pacific, eastern Atlantic, Southern Ocean, and coastal Florida for continental and marine aerosol, which are coded by a color key and a shape key. They must decode the graph and associate model parameters with real-world data. Coefficient C corresponds to the CCN concentration at 1% supersaturation. By abstracting typical C values for marine and continental conditions students learn to link the visual features in the graph to the physical meaning related to aerosol loading.
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