The Peer Pressure is a very simple calorimeter targeted at the search for LENR-active materials by the science crowd. For this purpose accuracy is important and repeatability is even more important. However, one should not be blindfolded by accuracy alone. By repeating the same experiment over and over again we get a clear few on the repeatability of energy out versus energy in measurements. If a particular experiment can be run a hundred times with a certain fixed power input and a measured energy output at, for example, 90% of the input, than a clear picture is obtained of the experimental standard deviation. When variations of the materials fed to the calorimeter vessel cause a change in the energy output, then repeated measurement can eventually confirm the statistical viability of the claim “more energy out than in”. Prerequisite for such approach is an fully automated setup, capable of autonomously controlling the input power, output power and gas pressure while logging all energy measurements and control temperatures.
Central to this automated experiment is the BeagleBone ARM controller which controls the solenoid gas valves for inlet and outlet of high-pressure gas, the electrical power fed into the cartridge heater, and the temperature measurements of the water cooling coil and reactor surroundings. The idea here is to create a web-based control and data-log application that can be used by the operator anywhere on the network. Multiple Peer Pressure systems would have multiple controllers and individual IP addresses. By logging into a particular Peer Pressure controller the operator can see its current measurements, a recent log and controls to start and stop recipes. Recipes are written in simplified Python and contain all details for the operation of a particular run. For example, a recipe could look like: Circulate hydrogen gas under moderate heating until all nickel-oxide is reduced to nickel, then heat up to set temperature and measure the temperature difference for hydrogen gas versus helium gas.
This application is what we are currently developing. Hardware and database operations are written in Python and the web-framework is made with Django. In addition we will need a graphical package to display the data logging results. For this MatPlotLib and R-language are considered as well as other open source graphical packages.
Developers are invited to help build the code and LENR scientists are invited to use it however they see fit. The code repository can be found here: