15 - New Functionalities Examples#
This journal includes short examples on how to use the new functionalities of version 0.4.0 of bifacial_radiance. The parts are:
Simulating Modules with Frames and Omegas
Improvements to irradiance sampling
Scanning full module (sensors on x)!
Different points in the front and the back
Full row scanning.
[1]:
import bifacial_radiance
import os
from pathlib import Path
testfolder = str(Path().resolve().parent.parent / 'bifacial_radiance' / 'TEMP' / 'Tutorial_15')
if not os.path.exists(testfolder):
os.makedirs(testfolder)
print ("Your simulation will be stored in %s" % testfolder)
Your simulation will be stored in C:\Users\sayala\Documents\GitHub\bifacial_radiance\bifacial_radiance\TEMP\Tutorial_15
I. Simulating Frames and Omegas#
The values for generating frames and omegas are described in the makeModule function, which is where they are introduced into the basic module unit. This diagram shows how they are measured.
[2]:
module_type = 'test-module'
frameParams = {'frame_material' : 'Metal_Grey',
'frame_thickness' : 0.05,
'nSides_frame' : 4,
'frame_width' : 0.08}
frameParams = {'frame_material' : 'Metal_Grey',
'frame_thickness' : 0.05,
'nSides_frame' : 4,
'frame_width' : 0.08}
omegaParams = {'omega_material': 'litesoil',
'x_omega1' : 0.4,
'mod_overlap' : 0.25,
'y_omega' : 1.5,
'x_omega3' : 0.25,
'omega_thickness' : 0.05,
'inverted' : False}
tubeParams = { 'visible': True,
'axisofrotation' : True,
'diameter' : 0.3
}
demo = bifacial_radiance.RadianceObj('tutorial_15', testfolder)
mymodule = demo.makeModule(module_type,x=2, y=1, xgap = 0.02, ygap = 0.15, zgap = 0.3,
numpanels = 2, tubeParams=tubeParams,
frameParams=frameParams, omegaParams=omegaParams)
path = C:\Users\sayala\Documents\GitHub\bifacial_radiance\bifacial_radiance\TEMP\Tutorial_15
Module Name: test-module
Module test-module updated in module.json
Pre-existing .rad file objects\test-module.rad will be overwritten
Alternatively, the paremeters can be passed from an Object Oriented Approach as follows:
[3]:
mymodule.addTorquetube(visible = True, axisofrotation = True, diameter = 0.3)
mymodule.addOmega(omega_material = 'litesoil', x_omega1 = 0.4, mod_overlap = 0.25,
y_omega = 1.5, x_omega3 = 0.25, omega_thickness = 0.05, inverted = False)
mymodule.addFrame(frame_material = 'Metal_Grey', frame_thickness = 0.05, nSides_frame = 4, frame_width = 0.08)
Module test-module updated in module.json
Pre-existing .rad file objects\test-module.rad will be overwritten
Module test-module updated in module.json
Pre-existing .rad file objects\test-module.rad will be overwritten
Module test-module updated in module.json
Pre-existing .rad file objects\test-module.rad will be overwritten
Let’s add the rest of the scene and go until OCT, so it can be viewed with rvu:
[4]:
demo.setGround(0.2)
epwfile = demo.getEPW(lat = 37.5, lon = -77.6)
metdata = demo.readWeatherFile(epwfile, coerce_year = 2021)
demo.gendaylit(4020)
sceneDict = {'tilt':0, 'pitch':3, 'clearance_height':3,'azimuth':90, 'nMods': 1, 'nRows': 1}
scene = demo.makeScene(mymodule,sceneDict)
demo.makeOct()
Loading albedo, 1 value(s), 0.200 avg
1 nonzero albedo values.
Getting weather file: USA_VA_Richmond.724010_TMY2.epw
... OK!
8760 line in WeatherFile. Assuming this is a standard hourly WeatherFile for the year for purposes of saving Gencumulativesky temporary weather files in EPW folder.
Coercing year to 2021
Saving file EPWs\metdata_temp.csv, # points: 8760
Calculating Sun position for Metdata that is right-labeled with a delta of -30 mins. i.e. 12 is 11:30 sunpos
Created tutorial_15.oct
[4]:
'tutorial_15.oct'
To view the module from different angles, you can use the following rvu commands in your terminal:
rvu -vp -7 0 3 -vd 1 0 0 Sim1.oct
rvu -vp 0 -5 3 -vd 0 1 0 Sim1.oct
[5]:
## Comment any of the ! line below to run rvu from the Jupyter notebook instead of your terminal.
## Simulation will stop until you close the rvu window(s).
#!rvu -vp -7 0 3 -vd 1 0 0 Sim1.oct
#!rvu -vp 0 -5 3 -vd 0 1 0 Sim1.oct
II. Improvements to irradiance sampling#
The key ideas here are:
moduleAnalysis()returns two structured dictionaries that have the coordinates necessary for analysis to know where to smaple. On the new version, different values can be given for sampling accross the collector slope (y), for both front and backs by using a single value or an array insensorsy.Furthermore, now scanning on the module’s x-direction is supported, by setting the variables
sensorsxas an singel value or an array.
When the sensors differ between the front and the back, instead of saving one .csv with results, two .csv files are saved, one labeled “_Front.csv” and the other “_Back.csv”.
To know more, read the functions documentation.
We’ll take advantage of Simulation 1 testfolder, Radiance Objects and sky, but let’s make a simple module and scene and model it through from there.
[6]:
mymodule = demo.makeModule(name='test-module',x=2, y=1)
sceneDict = {'tilt':0, 'pitch':6, 'clearance_height':3,'azimuth':180, 'nMods': 1, 'nRows': 1}
Module Name: test-module
Module test-module updated in module.json
Pre-existing .rad file objects\test-module.rad will be overwritten
[7]:
scene = demo.makeScene(mymodule,sceneDict)
octfile = demo.makeOct()
analysis = bifacial_radiance.AnalysisObj() # return an analysis object including the scan dimensions for back irradiance
Created tutorial_15.oct
Same sensors front and back, two sensors accross x#
[8]:
name='2222'
sensorsy_front = 2
sensorsy_back = 2
sensorsx_front = 2
sensorsx_back = 2
sensorsy = [sensorsy_front, sensorsy_back]
sensorsx = [sensorsx_front, sensorsx_back]
frontscan, backscan = analysis.moduleAnalysis(scene, sensorsy = sensorsy, sensorsx=sensorsx)
frontDict, backDict = analysis.analysis(octfile = octfile, name = name, frontscan = frontscan,
backscan = backscan)
print('\n--> RESULTS for Front and Back are saved on the same file since the sensors match for front and back')
print('\n', bifacial_radiance.load.read1Result('results\irr_'+name+'.csv'))
Linescan in process: 2222_Front
Linescan in process: 2222_Back
Saved: results\irr_2222.csv
--> RESULTS for Front and Back are saved on the same file since the sensors match for front and back
x y z rearZ mattype \
0 -0.333333 -0.166667 3.021 2.999 a0.0.a0.test-module.6457
1 -0.333333 0.166667 3.021 2.999 a0.0.a0.test-module.6457
2 0.333333 -0.166667 3.021 2.999 a0.0.a0.test-module.6457
3 0.333333 0.166667 3.021 2.999 a0.0.a0.test-module.6457
rearMat Wm2Front Wm2Back Back/FrontRatio
0 a0.0.a0.test-module.2310 187.6707 36.64622 0.195268
1 a0.0.a0.test-module.2310 187.6692 36.63796 0.195225
2 a0.0.a0.test-module.2310 187.6691 36.62679 0.195166
3 a0.0.a0.test-module.2310 187.6661 36.61245 0.195093
Different sensors front and back, two sensors accross x#
[9]:
name='2412'
sensorsy_front = 2
sensorsy_back = 4
sensorsx_front = 1
sensorsx_back = 2
sensorsy = [sensorsy_front, sensorsy_back]
sensorsx = [sensorsx_front, sensorsx_back]
frontscan, backscan = analysis.moduleAnalysis(scene, sensorsy=sensorsy, sensorsx=sensorsx)
frontDict, backDict = analysis.analysis(octfile = octfile, name = name, frontscan = frontscan,
backscan = backscan)
print('\n--> RESULTS for Front and Back are saved on SEPARATE file since the sensors do not match for front and back')
print('\nFRONT\n', bifacial_radiance.load.read1Result('results\irr_'+name+'_Front.csv'))
print('\nBACK\n', bifacial_radiance.load.read1Result('results\irr_'+name+'_Back.csv'))
Linescan in process: 2412_Front
Linescan in process: 2412_Back
Saved: results\irr_2412_Front.csv
Saved: results\irr_2412_Back.csv
--> RESULTS for Front and Back are saved on SEPARATE file since the sensors do not match for front and back
FRONT
x y z mattype Wm2Front
0 2.041078e-17 -0.166667 3.021 a0.0.a0.test-module.6457 187.5944
1 -2.041078e-17 0.166667 3.021 a0.0.a0.test-module.6457 187.5929
BACK
x y z rearMat Wm2Back
0 -0.333333 -0.3 2.999 a0.0.a0.test-module.2310 36.64336
1 -0.333333 -0.1 2.999 a0.0.a0.test-module.2310 36.63614
2 -0.333333 0.1 2.999 a0.0.a0.test-module.2310 36.62893
3 -0.333333 0.3 2.999 a0.0.a0.test-module.2310 36.62171
4 0.333333 -0.3 2.999 a0.0.a0.test-module.2310 36.63350
5 0.333333 -0.1 2.999 a0.0.a0.test-module.2310 36.62319
6 0.333333 0.1 2.999 a0.0.a0.test-module.2310 36.61286
7 0.333333 0.3 2.999 a0.0.a0.test-module.2310 36.60253
III. Making Analysis Function for Row#
Let’s explore how to analyze easily a row with the new function analyzeRow. As before, we are not repeating functions alreayd called above, just re-running the necessary ones to show the changes.
[10]:
sceneDict = {'tilt':0, 'pitch':30, 'clearance_height':3,'azimuth':90, 'nMods': 3, 'nRows': 3}
scene = demo.makeScene(mymodule,sceneDict)
octfile = demo.makeOct()
Created tutorial_15.oct
The function requires to know the number of modules on the row
[11]:
sensorsy_back=1
sensorsx_back=1
sensorsy_front=1
sensorsx_front=1
sensorsy = [sensorsy_front, sensorsy_back]
sensorsx = [sensorsx_front, sensorsx_back]
rowscan = analysis.analyzeRow(name = name, scene = scene, sensorsy=sensorsy, sensorsx = sensorsx,
rowWanted = 1, octfile = octfile)
Linescan in process: 2412_Module_0_Front
Linescan in process: 2412_Module_0_Back
Saved: results\irr_2412_Module_0.csv
Linescan in process: 2412_Module_1_Front
Linescan in process: 2412_Module_1_Back
Saved: results\irr_2412_Module_1.csv
Linescan in process: 2412_Module_2_Front
Linescan in process: 2412_Module_2_Back
Saved: results\irr_2412_Module_2.csv
rowscan is now a dataframe containing the values of each module in the row. Check the x, y and