The feed in tariffs are lucrative. However, the hydropower development in Japan is not straightforward due to fragmented government offices requiring permissions from multiple agencies.
River legislation and management
The new “River Law” started since 1964 to manage water resources in Japan. It has been revised multiple times.
The River Law classifies the rivers into two categories A and B. Class A rivers are administered by the Minister of Construction (MLIT). There are 17798 rivers grouped into 109 river systems in this category totaling to about 87,150km. Class B rivers are administered by the prefectural government. There are 6,631 rivers grouped in 2,691 river systems in this category totaling to about 35,700 km. Some section of rivers (both A and B) may be administered by local cities or villages.
Once the water is extracted from the river, it is managed by other laws. A list of ministries and agencies handling the water resource are listed in table below:
MILIT  It is responsible for over all water resource development 
MOE  Development of guidelines, policy and planning for water conservation and setting of water quality standards. 
Ministry of health  Regulation of domestic water supply facilities 
METI  Regulation of industrial water supply facilities 
Ministry of agriculture  Regulation of agricutural water and conservation of forest water resorces 
Japan water agency  It is responsible for the supply of safe, quality water at a reasonable price. It is engaged in the construction and refurbishment of major dams for water utilization (for domestic, industrial and agricultural use) and river management purposes (flood control, maintenance and environmental flow). 
Interministerial Liaison Council  This is responsible to study how procedures could be simplified fir water resource development 
Base on:
An octave (or matlab) code to generate such plot is given below. Basically, it requires to plot each entity separatly. The data should be normalized to set equal scale.
%shaded plot
clear all;clf; % clear figure (just in case something was there before)
epdata=[0.0008959 0.00059726 0.0013775 0.00080342 0.002345 0.0012454 0.0014801 0.00097711 0.00096319 0.00030882 0.0015608 0.00099932 0.00082923 0.00105 0.0013915 0.00021562 0.0011739 0.0015718 0.00087249 0.0016794 0.00036352 0.00059309 0.0011483 0.0010326 0.00034531 0.0020304 0.0014554 0.00092798 0.0012557 0.0016694 0.0010306 0.001245 0.0013191 0.0016508 0.0010848 0.0018287 0.0010912 0.0014441 0.0012839 0.00089405 0.0024034 0.0015758 0.00026981 0.0014717 0.0023293 0.0020914 0.00076314 0.00056843 0.0014572 0.00083516 0.0012168 0.0013884 0.0012994 0.0010078 0.00045422 0.0015285 0.00061293 0.00091066 0.0014499 0.0010975 0.001501 0.0012706 0.0019573 0.0019029 0.0013711 0.0019286 0.00099036 0.0016533 0.001049 0.0015944 0.0016858 0.0014946 0.000314 0.0016982 0.0012176 0.0016377 0.0012109 0.0015727 0.001435 0.0010661 0.0012172 0.0020125]
tcdata=[31.007 17.279 71.559 42.942 30.757 64.937 56.682 34.429 48.118 51.053 46.382 70.517 49.346 56.116 38.708 39.931 61.857 52.765 58.071 55.669 34.416 38.345 36.623 42.745 66.059 83.693 40.504 62.218 64.917 20.47 65.233 37.162 69.894 27.246 3.5394 65.416 65.68 43.018 95.189 59.176 59.944 56.573 53.296 38.949 41.054 39.729 28.056 43.599 59.708 46.291 49.449 48.566 57.104 51.747 68.345 48.032 63.941 41.263 26.673 48.541 31.849 56.63 39.846 49.557 80.801 37.334 37.459 36.248 57.355 39.958 32.182 24.017 75.422 61.597 43.402 11.902 58.988 42.176 17.418 44.48 56.916 41.854]
tldata=[61.235 77.001 86.213 75.22 43.764 67.211 76.459 52.866 77.028 54.373 34.241 90.087 80.787 60.298 75.159 107.12 84.327 39.385 104.76 66.38 62.929 42.629 88.112 83.559 59.862 85.192 50.859 55.306 66.787 56.848 82.743 95.426 112.39 44.012 70.265 54.671 76.631 76.642 88.841 39.25 56.877 60.893 104.51 59.836 63.706 71.222 63.624 32.153 70.074 79.584 72.205 72.182 73.233 67.998 80.533 40.931 63.258 42.223 79.662 81.851 52.266 80.383 83.354 57.363 93.174 64.228 87.165 93.264 79.508 67.241 98.564 86.5 37.035 59.61 64.813 68.867 72.937 81.84 61.348 92.968 57.552 64.609]
dpdata=[0.016616 0.0012712 0.018897 0.01016 0.092141 0.024361 0.023951 0.026684 0.013089 0.0056231 0.065296 0.011672 0.0096546 0.023527 0.018468 0.0022514 0.015527 0.056125 0.0052227 0.0348 0.0046483 0.020469 0.0072833 0.010084 0.0052599 0.030438 0.044018 0.022032 0.024795 0.031457 0.014002 0.0055563 0.0096733 0.063971 0.003394 0.046408 0.017231 0.019991 0.016868 0.028567 0.061399 0.036514 0.00094998 0.034091 0.050397 0.03461 0.011083 0.023391 0.027504 0.0096521 0.020398 0.023604 0.022343 0.018593 0.0046534 0.055475 0.011241 0.03279 0.0084095 0.013231 0.043265 0.018161 0.020591 0.04901 0.016887 0.038989 0.0064535 0.0089957 0.014825 0.029465 0.0045653 0.0031735 0.013363 0.040035 0.024299 0.00078227 0.020854 0.018003 0.015243 0.0074379 0.02939 0.042169]
% normalize
ep=epdata/min(epdata);
tc=tcdata/max(tcdata);
tl=tldata/max(tldata);
dp=dpdata/min(dpdata);
x=[0 1 2 3.5]*7;
x1=ones(1,length(ep))*x(1);
x2=ones(1,length(ep))*x(2);
x3=ones(1,length(ep))*x(3);
x4=ones(1,length(ep))*x(4);
%calculate mean
m_ep=mean(ep);
m_tc=mean(tc);
m_tl=mean(tl);
m_dp=mean(dp);
%standard deviation
sd_ep=std(ep);
sd_tc=std(tc);
sd_tl=std(tl);
sd_dp=std(dp);
%coords to plot normal distribution
nd_x_ep=[m_ep4*sd_ep:sd_ep/20:m_ep+4*sd_ep]
nd_y_ep=1/sd_ep/(2*pi())^0.5*exp((nd_x_epm_ep).^2/2/sd_ep^2)
nd_x_tc=[m_tc4*sd_tc:sd_tc/20:m_tc+4*sd_tc]
nd_y_tc=1/sd_tc/(2*pi())^0.5*exp((nd_x_tcm_tc).^2/2/sd_tc^2)
nd_x_tl=[m_tl4*sd_tl:sd_tl/20:m_tl+4*sd_tl]
nd_y_tl=1/sd_tl/(2*pi())^0.5*exp((nd_x_tlm_tl).^2/2/sd_tl^2)
nd_x_dp=[m_dp4*sd_dp:sd_dp/20:m_dp+4*sd_dp]
nd_y_dp=1/sd_dp/(2*pi())^0.5*exp((nd_x_dpm_dp).^2/2/sd_dp^2)
figure 1
% plot vertical lines
hold on;
plot (x1,ep,"o");
plot (x2,tc,"o");
plot (x3,tl,"o");
plot (x4,dp,"o");
%plot connecting lines
p=[ep; tc; tl; dp ];
plot (x, p, "color",[.8 .8 .8] );
%plot the normal disribution
patch(nd_y_ep*2+x(1),nd_x_ep,'facecolor',[1 1 0.8],'edgecolor',[1 0 0], 'facealpha',0.3)
patch(nd_y_tc*2+x(2),nd_x_tc,'facecolor',[1 1 0.8],'edgecolor',[1 0 0], 'facealpha',0.3)
patch(nd_y_tl*2+x(3),nd_x_tl,'facecolor',[1 1 0.8],'edgecolor',[1 0 0], 'facealpha',0.3)
patch(nd_y_dp*2+x(4),nd_x_dp,'facecolor',[1 1 0.8],'edgecolor',[0 0 1], 'facealpha',0.3,"linewidth", 3)
%yaxis lines
ymin=0.5;ymax=1.5;
plot ([x(1) x(1)],[ymin ymax],"color",[0 0 0] );
plot ([x(2) x(2)],[ymin ymax],"color",[0 0 0] );
plot ([x(3) x(3)],[ymin ymax],"color",[0 0 0] );
plot ([x(4) x(4)],[ymin ymax],"color",[0 0 0] );
%other properties
ylim([ymin ymax])
axis off;
set(gca, "linewidth", 2, "fontsize", 12);
%labels
%xlabels
text(x(1)+0.3,ymin,"Strain","color","black","fontsize", 18)
text(x(2)+0.3,ymin,"TC","color","black","fontsize", 18)
text(x(3)+0.3,ymin,"TL","color","black","fontsize", 18)
text(x(4)3.5,ymin,"Max. Deflection","color","black","fontsize", 18)
%informations
text(x(1)+0.3,ymax.2,["Mean=" num2str(0.0012, "%5.2e") char(10) "SD=" num2str(0.005, "%5.2e")],"color","black","fontsize", 15)
text(x(2)+0.3,ymax.2,["Mean=" num2str(50, "%5.0f") char(10) "SD=" num2str(15, "%5.0f") " years"],"color","black","fontsize", 15)
text(x(3)+0.3,ymax.2,["Mean=" num2str(70, "%5.0f") char(10) "SD=" num2str(20, "%5.0f") " years"],"color","black","fontsize", 15)
text(x(4)3.5,ymax.2,["Mean=" num2str(mean(dpdata), "%5.2e") char(10) "SD=" num2str(std(dpdata), "%5.2e") " m"],"color","black","fontsize", 15)
hold off;
print dpng figure1.png
]]>Once born we slowly evolve physically by gaining tissues and bones. The food we eat is transformed into the machinaries of body. This indicates that the entropy or randomness decreases over time to create a useful thing. But once we cross certain age decay starts and the biological machinaries starts to disfunction. It seems decay and dissolution is the final destiny. And in long term degeneration is imenent.
If so how about the evolution theory? Why should a simple being evolve to a complex one? More natural way would be a complex being degenerate into simple ones. Because this is the way we observe, its easy to break things than to create one. May be we human too are degenerated form of more complex superhumans, not the complex form of monkeys.
]]>2. Analysis of dam for varying water level
This is a typical analysis of dam where water level is assumed to fluctuate with time. Since two variables changes i.e. time and water depth, we need to make an external file to read the pressure (load). This tutorial will show how to do it.
function retriveUnreadMessages() {
// imports subject, from (email), received date of all unread email in inbox of gmail account
// the data is then written in the google spreadsheet
//reference:https://developers.google.com/appsscript/reference/spreadsheet/spreadsheet#appendRow(Object)
//
//google sheet part
var ss=SpreadsheetApp.getActiveSheet();
var date1=new Date().toISOString().slice(0, 10);
ss.appendRow(["***** new execution = ",date1,"*****"]);// append a new information line where the new execution is done because data is appended at the end
//gmail part
// Log the subject lines of your Inbox
var threads = GmailApp.getInboxThreads();
// number of message to retrive
var n= threads.length;
for (var i = 0; i < n; i++) {
// check if there is unread messages in a thread
if(threads[i].isUnread)
{
var subject1=threads[i].getFirstMessageSubject(); // subjet of thread
var email1=threads[i].getMessages(); // messages inside the thread
// check if the individual message is unread
for (var j = 0; j < email1.length; j++) {
if(email1[j].isUnread)
{
var message_sender=email1[j].getFrom();
var message_sendDateTime=email1[j].getDate();
}
ss.appendRow([i,j,subject1,message_sender,message_sendDateTime]); //write data to spreadsheet.. it is appended to new row
}
}
}
}
Mandelbrot
function mandelbrotmain()
clear variables; clc;
maxit=200;
x=[2,1];
y=[1 1];
xpix=601;
ypix=401;
x=linspace(x(1),x(2),xpix);
y=linspace(y(1),y(2),ypix);
[xG, yG]=meshgrid(x,y);
c=mb(maxit,xG,yG);
figure
imagesc(x,y,c);
colormap([1 1 1;0 0 0]);
axis on;
grind on;
endfunction
function count=mb(maxItr,xG,yG)
c=xG+1i*yG;
count=ones(size(c));
z=c;
for n=1:maxItr
z=z.*z+c;
inside=abs(z)<=2;
count=count+inside;
endfor
endfunction
Sierpinski triangle
clc;
clear variables;
close windows;
clf
N=500;
x=zeros(1,N);y=x;r=x;
for a=2:N
c=randi([0 2]);
r(a)=c;
switch c
case 0
x(a)=0.5*x(a1);
y(a)=0.5*y(a1);
case 1
x(a)=0.5*x(a1)+.25;
y(a)=0.5*y(a1)+sqrt(3)/4;
case 2
x(a)=0.5*x(a1)+.5;
y(a)=0.5*y(a1);
end
end
plot(x,y,’o’)
title(‘Sierpinski’s triangle’)
legend(sprintf(‘N=%d Iterations’,N))
Fractal tree
function treemain
tic
clear all;
depth = 7;
figure 1;
hold on;
drawTree2(0, 0, 90, depth);
toc;
endfunction
function drawTree2(x1, y1, angle, depth)
deg_to_rad = pi / 180.0;
rot=30; #degrees :: rotation from second iteration
branchLength=100*depth;
if (depth != 0)
x2 = x1 + cos(angle * deg_to_rad) * branchLength;
y2 = y1 + sin(angle * deg_to_rad) * branchLength ;
line([x1, x2], [y1, y2],’LineWidth’,depth);
drawTree2(x2, y2, angle – rot, depth – 1);
drawTree2(x2, y2, angle + rot, depth – 1);
endif
endfunction
A note on fractal (from HH Hardey):
There are upper and lower limits to describe natural objects by fractals. A figure may look like a fern leaf, but continued generation of the “leaf on larger and larger scales produces only a larger and larger fern leaf. A fern “plant” will never be produced. Similarly, there is a smallest scale to which a fractal description applies. As the overall fern leaf pattern is repeated on smaller and smaller scales, eventually the scale of a single fern plant cell will be reached. A single cell does not look like the overall shape.
To use intel MKL library inside visual studio do the followings:
1. Right click the project>Properties
2. In Property page>Fortran>Libraries set (a) Runtime library : Multithreaded and (b) Use Intel MKL: Sequential
Once this setting is done, you can compile the file in DEBUG mode. Repeat the same process (12) for RELEASE mode too once more.(Note exe of DEBUG mode is far slower than exe of RELEASE mode. The settings to set are listed below.
To do the same thing MANUALLY (which gives more insight to the code) do the following in each DEBUG and RELEASE mode.
1. Property page>Fortran>General>Additional Include Directories: C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2019.4.245\windows\mkl\include
2. Property page>Fortran>Preprocessor>Preprocess source file: Yes
3. Property page>Linker>General>Additional Library Directories:C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2019.4.245\windows\mkl\lib\ia32_win
4. Property page>Linker>Input>Additional Dependencies: mkl_intel_c.lib mkl_intel_thread.lib mkl_core.lib libiomp5md.lib
The manual setting is described in the video as well:
For method to install Fortran in visual studio see this post.
]]>Here,
Similarly, from last two figures
or,
or, (= weight of equal volume of water as that of solid)
Now,
i.e.
FIELD NAME  AVAILABLE VARIABLES  APPLICABLE MODEL TYPES  NON APPLICABLE MODEL 
EFGE_ELNO  N,VY,VZ,MT, MFY,MFZ  POU_D_E,  3D, 2D 
SIEF_ELNO  POU_D_T, POU_D_E POU_D_TG, POU_D_T_GD 

FORC_F  FX FY FZ MX MY MZ  
DEPL_R  DX DY DZ DRX DRY MARTINI DRZ  
SIEF_R  SIXX SIYY SIZZ SIXY SIXZ SIYZ  3D  
SIEF_R  NR VY VZ MT MFY MFZ  Discrete elements  
SIEF_R  NXX NYY NXY MXX MYY MXY QX QY  HULL  
SIEF_R  BR  Beam with wrapping  
SIEF_R  POU_D_T, POU_D_E POU_D_TG, POU_D_T_GD 

SIEF_ELGA  3D,2D, DKT, DST, Q4G, Q4GG, COQUE_3D, BARR, 2D_BARR  POU_D_T, POU_D_E POU_D_TG, POU_D_T_GD 

SIGM_ELGA  3D,2D, DKT, DST, Q4G, Q4GG, COQUE_3D  POU_D_T, POU_D_E POU_D_TG, POU_D_T_GD 

SIGM_ELNO  POU_D_T, POU_D_E POU_D_TG, POU_D_T_GD 

EFGE_ELNO
Effort 
3D, 2D  
EFGE_ELNO  3D, 2D  
FORC_NODA
nodal forces 
DX DY DZ DRX DRY MARTINI DRZ  
EPSI_ELGA  
EPSG_ELGA  3D  Hulls, plates (except DKTG and Q4GG), PIPES, Multifibre beams 

EPME_ELGA  3D  Hulls, plates (except DKTG and Q4GG) 

EPMG_ELGA  3D  Hulls, plates (except DKTG and Q4GG), PIPES, Multifibre beams 

EPSP_ELGA  3D  Hulls, plates (except DKTG and Q4GG) 

EPSI_ELNO

3D  
EPSG_ELNO  3D  Hulls, plates (except DKTG and Q4GG), PIPES, Multifibre beams 

EPME_ELNO  3D  Hulls, plates (except DKTG and Q4GG) 

EPMG_ELNO  3D  Hulls, plates (except DKTG and Q4GG), PIPES, Multifibre beams 

EPSP_ELNO  3D  Hulls, plates (except DKTG and Q4GG) 

DEGE_ELGA

Plates, Coques1D 
3D  
DEGE_ELNO

Plates, Coques1D 
3D  
VARI_ELGA  BARR, 2D_BARR  
Some clues:
DEGE : deformations generalized on the elements of beam or hull
ELNO: Node calculation carried out by extrapolation with the nodes of the quantities at the points of Gauss
ELGA:Gauss point
NOEU:
EF~:Effort
SI~: Stress
]]>