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PV Cell Modelling

VI plot, PV plot of PV array

ROBOT MARS ROVER

Ce dossier est destiné aux enseignants de Sciences de L'ingénieur souhaitant exploiter en classe la plateforme Mars Rover créée par MathWorks dans le cadre de la compétition « Mission On Mars » Robot Challenge.
Il contient:
• Une activité pédagogique sur la modélisation des paramètres moteur et la comparaison entre réel et modèle. Cette activité s'appuie sur le robot Mars Rover mais la démarche et l'activité peuvent être réutilisées sur d'autres robots mobiles.
• les modèles Simulink (équations et fonction de transfert) et Simscape des moteurs du robot Mars Rover
• Le modèle SimMechanics idéal du robot Mars Rover (pas de saturation, effet dynamique du robot sur l’axe moteur non pris en compte, patinage des roues absent)

A Simple Finite Volume Solver for Matlab

A simple Finite volume tool
This code is the result of the efforts of a chemical/petroleum engineer to develop a simple tool to solve the general form of convection-diffusion equation:
α∂ϕ/∂t+∇.(uϕ)+∇.(−D∇ϕ)+βϕ=γ
on simple uniform mesh over 1D, 1D axisymmetric (radial), 2D, 2D axisymmetric (cylindrical), and 3D domains.
The code accepts Dirichlet, Neumann, and Robin boundary conditions (which can be achieved by changing a, b, and c in the following equation) on a whole or part of a boundary:
a∇ϕ.n+bϕ=c.
It also accepts periodic boundary conditions.
The main purpose of this code is to serve as a handy tool for those who try to play with mathematical models, solve the model numerically in 1D, compare it to analytical solutions, and extend their numerical code to 2D and 3D with the minimum number of modifications in the 1D code.
The discretizaion schemes include
   * central difference
   * upwind scheme for convective terms
   * TVD schemes for convective terms

A short html document is available to get you started. Simply type
showdemo FVTdemo
after downloading and extracting the code.

A few calculus functions (divergence, gradient, etc) and averaging techniques (arithmetic average, harmonic average, etc) are available, which can be helpful specially for solving nonlinear or coupled equations or implementing explicit schemes.

I have used the code to solve coupled nonlinear systems of PDE. You can find some of them in the Examples/advanced folder.

There are a few functions in the 'PhysicalProperties' folder for the calculation of the physical properties of fluids. Some of them are not mine, which is specified inside the file.

I'll try to update the documents regularly, in the github repository. Please give me your feedback/questions by writing a comment in my weblog: <http://fvt.simulkade.com/>
Special thanks: I vastly benefited from the ideas behind Fipy <http://www.ctcms.nist.gov/fipy/>, a python-based finite volume solver.

To start the solver, download and extract the zip archive, open and run 'FVToolStartUp' function.
To see the code in action, copy and paste the following in your Matlab command window:

clc; clear;
L = 50; % domain length
Nx = 20; % number of cells
m = createMesh3D(Nx,Nx,Nx, L,L,L);
BC = createBC(m); % all Neumann boundary condition structure
BC.left.a(:) = 0; BC.left.b(:)=1; BC.left.c(:)=1; % Dirichlet for the left boundary
BC.right.a(:) = 0; BC.right.b(:)=1; BC.right.c(:)=0; % Dirichlet for the right boundary
D_val = 1; % value of the diffusion coefficient
D = createCellVariable(m, D_val); % assign the diffusion coefficient to the cells
D_face = harmonicMean(m, D); % calculate harmonic average of the diffusion coef on the cell faces
Mdiff = diffusionTerm(m, D_face); % matrix of coefficients for the diffusion term
[Mbc, RHSbc] = boundaryCondition(m, BC); % matix of coefficients and RHS vector for the BC
M = Mdiff + Mbc; % matrix of cefficients for the PDE
c = solvePDE(m,M, RHSbc); % send M and RHS to the solver
visualizeCells(m, c); % visualize the results

You can find some animated results of this code in my youtube channel:
https://www.youtube.com/user/processsimulation/videos

Help Reading an Input File
Hi Guys,

I have an input file (displayed below) for which I want to read the data (which includes text) and ignore the header lines.
I have opened the file with:
LinkFile = importdata('EPANETreport',' ',17); %17 Header lines
LinkFile.data gives me the matrix of the first set of numbers

If I then do:
NodeFile = importdata('EPANETreport',' ',28); %28 Header lines
NodeFile.data gives me the matrix of the second set of numbers up until but not including 'reservior', and then stops.

So how can I include the text 'reservior' and 'tank' into my second matrix, or;
How can I save the 3 matrices including their text elements?

Thanks heaps,
Jesse.

  Page 1 7/30/2015 1:46:04 PM
  **********************************************************************
  * E P A N E T *
  * Hydraulic and Water Quality *
  * Analysis for Pipe Networks *
  * Version 2.0 *
  **********************************************************************
  
  Input File: network.net
  
  
  
  Link - Node Table:
  ----------------------------------------------------------------------
  Link Start End Length Diameter
  ID Node Node m mm
  ----------------------------------------------------------------------
  1 5 3 4000 1500
  2 3 4 3500 1200
  3 4 6 2200 1000
  4 3 1 1500 1000
  5 4 2 1300 800
  
  Node Results:
  ----------------------------------------------------------------------
  Node Demand Head Pressure Quality
  ID LPS m m
  ----------------------------------------------------------------------
  3 0.00 30.55 30.55 0.00
  4 0.00 25.03 25.03 0.00
  5 7.04 30.00 0.00 0.00 Reservoir
  6 57.99 12.00 0.00 0.00 Reservoir
  1 -42.09 34.39 10.00 0.00 Tank
  2 -22.94 28.24 10.00 0.00 Tank
  
  Link Results:
  ----------------------------------------------------------------------
  Link Flow VelocityUnit Headloss Status
  ID LPS m/s m/km
  ----------------------------------------------------------------------
  1 -7.04 0.00 0.14 Open
  2 35.06 0.03 1.58 Open
  3 57.99 0.07 5.92 Open
  4 -42.09 0.05 2.56 Open
  5 -22.94 0.05 2.47 Open
Extracting coordinates of a certain values in a matrix
Good day,

I would like to extract coordinates of certain values from a matrix.

For instance, I have a 3x3 matrix, M,

M= [1 4 7;
     2 5 8;
     3 6 9]

and I want to extract the coordinates of these values,
p=[1; 8; 2; 1]

Thus, I tried,

ismember(M,p)
[i,j] = ind2sub(size(M),find(ismember(M,p)))

and I got this,

i =

     1
     2
     2


j =

     1
     1
     3

It seems that, they are the locations for the unique(p).

Instead, I need locations as in, (without using for loop)

i=
1
2
2
1

j=
1
3
1
1

Thanks.
Intuitive RGB color values from XKCD

This function returns the RGB triplet for almost any color. And unlike the way colors are somewhat officially defined (http://en.wikipedia.org/wiki/Web_colors), this data set describes returns values for the ways that colors are actually perceived on computer monitors. Color names are intuitive because they are based on a survey of over 200,000 user sessions, wherein respondents were shown colors and asked to fill in their word for that color. You won't find "puke green" on Wikipedia's list of web colors, despite the fact that it's a color name that people use and understand.
Data come from an XKCD survey described here: http://blog.xkcd.com/2010/05/03/color-survey-results/
A chart of available colors and their most common names can be found here: http://xkcd.com/color/rgb/

Syntax
RGB = rgb('Color Name')
RGB = rgb('Color Name 1','Color Name 2',...,'Color Name N')
RGB = rgb({'Color Name 1','Color Name 2',...,'Color Name N'})

Description
RGB = rgb('Color Name') returns the RGB triplet for a color described by 'Color Name'.

RGB = rgb('Color Name 1','Color Name 2',...,'Color Name N') returns an N by 3 matrix containing RGB triplets for each color name.

RGB = rgb({'Color Name 1','Color Name 2',...,'Color Name N'}) accepts list of color names as a character array.

newname(oldname)

Rename a variable & delete the old name. Essentially this is a shorter-keystroke version of "a=b;clear(b)" .
 Be warned that this function messes with the calling environment, a programming practice frowned upon by many.

IBCSO International Bathymetric Chart of the Southern Ocean Toolbox

This toolbox has functions to access, interpolate, and plot IBCSO DEM data (http://ibcso.org). See the documentation files (light bulb icons to the right) for function syntax, description, and examples. This is a plugin for Antarctic Mapping Tools for Matlab (AMT).

Feature based Dense Disparity Estimation

In this framework, a feature based dense disparity estimation scheme has been developed. This scheme incorporates segmentation and feature matching schemes for obtaining disparity in different sub-images. The initial features obtained using SURF detector is used for matching segments indirectly. Segments which do not contain any feature point are matched via epipolar constraint by randomly selecting keypoints in the reference image. The scheme is found to provide better texture conservation in the resultant dense image. The proposed algorithm sets a new scheme for computing dense depth from sparse disparity points and requires further improvement in terms of accuracy.
The main.m file needs to be run for obtaining the depth image directly. A readme file has also been provided which can help in providing more details.
Ref: "Poddar, Shashi; Sahu, Hemraj; Bangale, Mohit R; Kumar, Vipan; Kumar, Amod, "Feature based dense disparity estimation," Industrial Instrumentation and Control (ICIC), 2015 International Conference on , vol., no., pp.950,955, 28-30 May 2015
doi: 10.1109/IIC.2015.7150881

Sparse Locally Linear and Neighbor Embedding for Nonlinear Time Series Prediction

The submission contains the sparse coding program plus the support vector regression and bagging trees programs. It also contains the 16 data sets that were used in the paper
Citing: Waleed Fakhr, "Sparse Locally Linear and Neighbor Embedding for Nonlinear Time Series Prediction", ICCES 2015, December 2015.

"This paper proposes a dictionary-based L1-norm sparse coding for time series prediction which requires no training phase, and minimal parameter tuning, making it suitable for nonstationary and online prediction applications. The prediction process is formulated as a basis pursuit L1-norm problem, where a sparse set of weights is estimated for every test vector. Constrained sparse coding formulations are tried including sparse local linear embedding and sparse nearest neighbor embedding. 16 time series datasets are used to test the approach for offline time series prediction where the training data is fixed. The proposed approach is also compared to Bagging trees (BT), least-squares support vector regression (LSSVM) and regularized Autoregressive model. The proposed sparse coding prediction shows better performance than the LSSVM that uses 10-fold cross validation and significantly better performance than regularized AR and Bagging trees. In average, a few thousand sparse coding predictions can be done while the LSSVM is training."

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