"The real time data gathering provided by the HT14 software is a very good feature especially when temperature transients are involved, but getting the data into an Excel spreadsheet makes the equipment much more of an experimental platform and exploratory tool," he said.

Students export their data to a spreadsheet package developed by Dr. Zampino. This contains a more extensive heat transfer analysis of the experimental data. The students use the spreadsheet to analyze and report the data in tabular and graphical formats.

The basic function of the spreadsheet is to take the experimental data and perform a conservation of energy analysis, determine the percentage of the total heat transport by radiation and convection, determine the experimental Nusselt number for the heat transfer, and the Reynolds number for the air flow. The experimental Nusselt number is compared to one obtained using an empirical correlation.

"The key is to plot the Nusselt number as a function of the Reynolds numbers and compare with empirical correlations," said Marc."Students use their text books and even research papers to obtain empirical correlations."

Another of several examples his students use is to plot the percentages of radiation and convection heat transfer against the Reynolds number of the airflow. This produces two curves which crossover at a specific Reynolds number.

This plot graphically illustrates how radiation and convection are competing forms of heat transfer, with radiation dominating at low air speeds, and eventually convection becoming the dominant mode of heat transfer. Performing this experiment at multiple power levels (i.e. thermal loads) shows that the crossover point between the two heat transfer modes shifts with Reynolds number. Using an external spreadsheet with the HT-14 provides the opportunity for students to investigate subtle secondary phenomena by the ability to plot the data in various ways and to determine empirical correlation for the trends they see.

The HT14 apparatus is also used to investigate natural convection with an analysis similar to that for forced convection. However, in this case, the students determine the Grashof number instead of the Reynolds number, illustrating the difference between airflow induced by buoyancy forces rather than by a pressure gradient. For advanced investigation, students can couple the results from the natural and forced convection experiments and the transition from natural to forced convection.

Dr. Zampino uses these techniques with a senior level class already conversant with heat transfer and fluids, with experimental teams limited to two or three students.

" We do the experiments quickly by taking only a small set of data which I collect and add to a larger data set. I distribute the larger data set over the web and the class works as a group with the collective data set." he said. "This promotes a sense of involvement for the students as they are part of an ongoing project which I am directing."

"The use of collective data sets, helps students to develop their spreadsheet skills, since they have to analyze data more efficiently. It also allows them to use statistical analysis and regression techniques."