CO2 Thermo: This app calculates thermodynamics properties of CO2. This first released version shows solubility of CO2 in water (g/L), compressibility factor, density (kg/m3), Enthalpy (KJ/Kg), Internal Energy (KJ/Kg), Entropy (KJ/Kg/K), Helmohltz energy (KJ/Kg), and Gibbs Free Energy (KJ/Kg) for given temperature (C) and pressure (bar). Including for academic education specially Chemical, Mechanical, and Petroleum engineering, Physics, Chemistry, and Geoscience students this app is developed targeting the audience of CO2 sequestration research, CO2 based geothermal energy industry, up- and mid-stream oil and gas industry, and many other industry applications where CO2 is used as a working fluid. The app can downloaded for free from https://play.google.com/store/apps/details?id=com.akand.co2cal&hl=en
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Privacy policy
Personal Information
We DO NOT collect, store or use any personal information while you visit, download or upgrade our applications and products.
We may use personal information submitted by you only for the following purposes: help us develop, deliver, and improve our products and services and supply higher quality service; manage online surveys and other activities you’ve participated in.
In the following circumstances, we may disclose your personal information according to your wish or regulations by law:
(1) Your prior permission;
(2) By the applicable law within or outside your country of residence, legal process, litigation requests;
(3) By requests from public and governmental authorities;
(4) To protect our legal rights and interests.
Non- Personal Information
We may collect and use non-personal information in the following circumstances. To have a better understanding in user’s behavior, solve problems in products and services, improve our products, services and advertising, we may collect non-personal information such as installed application name and package name, the data of install, frequency of use, country, equipment and channel.
If non-personal information is combined with personal information, we treat the combined information as personal information for the purposes of this Privacy Policy.
SciPylot: SciPylot is an interactive development environment for Python. Its interactivity and introspection make very suitable for scientists and engineers in order to do Python programming. This is an open source Matlab style console window and editor compatible with Windows/Linux/Mac OS X. It has multi-window colorized source browser, controlpane, and workspace browser. As a complete user-friendly IDE, among its numerous lucrative traits include object auto-completions, tooltips, calltips, syntax highlighting, multi-line command editing in the console, auto indents/dedents, sys.path browser, environment variable browser, integrated plotting features, inline find/replace and search in files features, printing both source codes and output files, debugging using winpdb, websupport and so on. For free hand drawing Pysketch has also been included with it. The SciPylot package is completely free and can be downloaded from https://github.com/Akand/SciPylot
OpenMG: In many large-scale computations, systems of equations arise in the form Au = b, where A is a linear operation to be performed on the unknown data u, producing the known right-hand side, b, which represents some constraint of known or assumed behavior of the system being modeled. Since such systems can be very large, solving them directly can be too slow. In contrast, a multigrid solver solves partially at full resolution, and then solves directly only at low resolution. This creates a correction vector, which is then interpolated to full resolution, where it corrects the partial solution. This project aims to create an open-source multigrid solver called OpenMG, written only in Python. The existing PyAMG multigrid implementation is a highly versatile, configurable, black-box solver, but is difficult to read and modify due to its C core. OpenMG is a pure Python experimentation environment for testing multigrid concepts, not a production solver. By making the code simple and modular, we make the algorithmic details clear. We thereby create an opportunity for education and experimentation with the partial solver (Jacobi, Gauss Seidel, SOR, etc.), the restriction mechanism, the prolongation mechanism, and the direct solver, or the use of GPGPUs, multiple CPUs, MPI, or grid computing. The resulting solver is tested on an implicit pressure reservoir simulation problem with satisfactory results. This is available in Github: https://github.com/tsbertalan/openmg.