Pinned Repositories
blynk-library
Blynk library for embedded hardware. Works with Arduino, ESP8266, Raspberry Pi, Intel Edison/Galileo, LinkIt ONE, Particle Core/Photon, Energia, ARM mbed, etc.
C-CODE_FOR_MEMBRANE_BASED_MODEL
Membrane-based dehumidification is currently being considered as a promising solution for the building application due to its low cost and very limited energy consumption. Developing a simple and efficient open-source code simulation tool is important for boosting the optimization and evaluation of such device in HVAC community. This paper reports a first-order physics based model which accounts for the fundamental heat and mass transfer of humid-air vapor at feed side to flow stream at permeate side. The current model comprises two membrane mass transfer submodels (i.e. microstructure model and performance map model); and it adopts a segment-by-segment methodology for discretizing heat and mass transfer governing equations. The model is capable of simulating both dehumidifiers and energy recovery ventilators with parallel-flow cross-flow, and counter-flow configurations. The model was validated with the measurements at appropriate device. The practices in dehumidification and energy recovery exchangers are also discussed. The model and open-source codes are expected to become a solid fundament for developing a more comprehensive and accurate membrane-based dehumidification in the future.
datasciencecoursera
datasharing
The Leek group guide to data sharing
FirstGit
prova
gauge2data
Convert a (timelapse) video of an analog gauge to a data series
hello-world
klima01
klimatest
ProgrammingAssignment2
Repository for Programming Assignment 2 for R Programming on Coursera
PyPD
A Python version of a pressure drop program for flow through pipes.
AlessandroGraizzaro's Repositories
AlessandroGraizzaro/blynk-library
Blynk library for embedded hardware. Works with Arduino, ESP8266, Raspberry Pi, Intel Edison/Galileo, LinkIt ONE, Particle Core/Photon, Energia, ARM mbed, etc.
AlessandroGraizzaro/C-CODE_FOR_MEMBRANE_BASED_MODEL
Membrane-based dehumidification is currently being considered as a promising solution for the building application due to its low cost and very limited energy consumption. Developing a simple and efficient open-source code simulation tool is important for boosting the optimization and evaluation of such device in HVAC community. This paper reports a first-order physics based model which accounts for the fundamental heat and mass transfer of humid-air vapor at feed side to flow stream at permeate side. The current model comprises two membrane mass transfer submodels (i.e. microstructure model and performance map model); and it adopts a segment-by-segment methodology for discretizing heat and mass transfer governing equations. The model is capable of simulating both dehumidifiers and energy recovery ventilators with parallel-flow cross-flow, and counter-flow configurations. The model was validated with the measurements at appropriate device. The practices in dehumidification and energy recovery exchangers are also discussed. The model and open-source codes are expected to become a solid fundament for developing a more comprehensive and accurate membrane-based dehumidification in the future.
AlessandroGraizzaro/datasciencecoursera
AlessandroGraizzaro/datasharing
The Leek group guide to data sharing
AlessandroGraizzaro/FirstGit
prova
AlessandroGraizzaro/gauge2data
Convert a (timelapse) video of an analog gauge to a data series
AlessandroGraizzaro/hello-world
AlessandroGraizzaro/klima01
klimatest
AlessandroGraizzaro/ProgrammingAssignment2
Repository for Programming Assignment 2 for R Programming on Coursera
AlessandroGraizzaro/PyPD
A Python version of a pressure drop program for flow through pipes.
AlessandroGraizzaro/r4ds
R for data science
AlessandroGraizzaro/scipy-lecture-notes
Tutorial material on the scientific Python ecosystem
AlessandroGraizzaro/TORCHE
TOolbox for Reactor Cross-Flow Heat Exchangers: Python Scripts for calculation of Pressure drop and Heat Transfer for crossflow tube bundles based on models found across the literature..
AlessandroGraizzaro/Udemy---Machine-Learning
Notebooks for Course
AlessandroGraizzaro/Udemy-notes
My udemy notebooks