ers can be applied from the right click Context Menu of either a component's input or output parameters. With the exception of <Principal> and <Degrees> they work exactly like their corresponding Grasshopper Component. When a I/O Modifier is applied to a parameter a visual Tag (icon) is displayed. If you hover over a Tag a tool tip will be displayed showing what it is and what it does.
The full list of these Tags:
1) Principal
An input with the Principal Icon is designated the principal input of a component for the purposes of path assignment.
For example:
2) Reverse
The Reverse I/O Modifier will reverse the order of a list (or lists in a multiple path structure)
3) Flatten
The Flatten I/O Modifier will reduce a multi-path tree down to a single list on the {0} path
4) Graft
The Graft I/O Modifier will create a new branch for each individual item in a list (or lists)
5) Simplify
The Simplify I/O Modifier will remove the overlap shared amongst all branches. [Note that a single branch does not share any overlap with anything else.]
6) Degrees
The Degrees Input Modifier indicates that the numbers received are actually measured in Degrees rather than Radians. Think of it more like a preference setting for each angle input on a Grasshopper Component that state you prefer to work in Degrees. There is no Output option as this is only available on Angle Inputs.
7) Expression
The Expression I/O Modifier allows you change the input value by evaluating an expression such as -x/2 which will have the input and make it negative. If you hover over the Tag a tool tip will be displayed with the expression. Since the release of GH version 0.9.0068 all I/O Expression Modifiers use "x" instead of the nickname of the parameter.
8) Reparameterize
The Reparameterize I/O Modifier will only work on lines, curves and surfaces forcing the domains of all geometry to the [0.0 to 1.0] range.
9) Invert
The Invert Input Modifier works in a similar way to a Not Gate in Boolean Logic negating the input. A good example of when to use this is on [Cull Pattern] where you wish to invert the logic to get the opposite results. There is no Output option as this is only available on Boolean Inputs.
…
n be obtained for curved NURBS surfaces as well as unconventional window configurations".
And I also noticed the following information form the optional input in the runEnergySimulation component.
"meshSettings_: Optional mesh settings for your geometry from any one of the native Grasshopper mesh setting components. These will be used to change the meshing of curved surfaces before they are run through EnergyPlus (note that meshing of curved surfaces is done since Energyplus is not able to calculate heat flow through non-planar surfaces). Default Grasshopper meshing is used if nothing is input here but you may want to decrease your calculation time by changing it to Coarse or increase your curvature definition (and calculation time) by making it finer".
1) My case is an one-story, rectangular-plan large hall (40m*70m*25m) with a curved roof. The roof surface is a part of a standard sphere and the walls and floor are all planar (the each wall has one curved edge as showed in the image).
For testing, I threw the original curved roof surface into daylight and energy simulations without making customized meshings, because I assumed that it might be automatically converted to meshs by Honeybee - Am I right? As showed in the image, how can I reduce the number of meshs in a proper way? Must two connected surfaces (i.e. wall and roof) be STRICTLY/SEAMLESSLY connected or not (considering different divisions of meshs in the respective surface)? - Is a connection tolerance allowed?
2) But, when I run the annual daylight simulation for this case, it gave me a lot of warnings "oconv: warning - zero area for polygon".- is that normal? and how to avoid this? Does the daylight simulation allow "curved NURBS surfaces"?
3) Moreover, when I run an energy simulation for this case, it costed extremely long time. It was just so long that I did not even have results out of one simulation. - I guessed it might be the problem caused by the curved roof surface (or automatic meshing?), but I don't have experience of converting a curved NURBS/spheral surface into correct meshs that can be recognized by Honeybee simulations (Daylight and Energy) in a proper way.
4) The large window on the wall was generated by the "_glzRatio". But the automatically generated wall meshs around this window are just too "fine", which might largely increase simulation time. Is there a proper way to get rid of it? (Considering that the size, shape and position of the window will have large influence on the daylight distribution in the building, it is worthy to keep the size, shape and position of the window as it should be in reality).
In sum, considering all above, could your please provide me some suggestions/tutorials/links that might be helpful for dealing with "curved NURBS surfaces" in Honeybee simulations.
Thank you all in advance!
Best,
Ding
…
ing the maps to the broader community.
At the moment, there are just a few known issues left that I have to fix for complex geometric cases but they should run smoothly for most energy models that you generate with Honeybee. Within the next month, I will be clearing up these last issues and, by the end of the month, there will be an updated youtube tutorial playlist on the comfort tools and how to use them.
In the meantime, there's an updated example file (http://hydrashare.github.io/hydra/viewer?owner=chriswmackey&fork=hydra_2&id=Indoor_Microclimate_Map) and I wanted to get you all excited with some images and animations coming out of the design part of my thesis. I also wanted to post some documentation of all of the previous research that has made these climate maps possible and give out some much deserved thanks. To begin, this image gives you a sense of how the thermal maps are made by integrating several streams of data for EnergyPlus:
(https://drive.google.com/file/d/0Bz2PwDvkjovJaTMtWDRHMExvLUk/view?usp=sharing)
To get you excited, this youtube playlist has a whole bunch of time-lapse thermal animations that a lot of you should enjoy:
https://www.youtube.com/playlist?list=PLruLh1AdY-Sj3ehUTSfKa1IHPSiuJU52A
To give a brief summary of what you are looking at in the playlist, there are two proposed designs for completely passive co-habitation spaces in New York and Los Angeles.
These diagrams explain the Los Angeles design:
(https://drive.google.com/file/d/0Bz2PwDvkjovJM0JkM0tLZ1kxUmc/view?usp=sharing)
And this video gives you and idea of how it thermally performs:
These diagrams explain the New York design:
(https://drive.google.com/file/d/0Bz2PwDvkjovJS1BZVVZiTWF4MXM/view?usp=sharing)
And this video shows you the thermal performance:
Now to credit all of the awesome people that have made the creation of these thermal maps possible:
1) As any HB user knows, the open source engines and libraries under the hood of HB are EnergyPlus and OpenStudio and the incredible thermal richness of these maps would not have been possible without these DoE teams creating such a robust modeler so a big credit is definitely due to them.
2) Many of the initial ideas for these thermal maps come from an MIT Masters thesis that was completed a few years ago by Amanda Webb called "cMap". Even though these cMaps were only taking into account surface temperature from E+, it was the viewing of her radiant temperature maps that initially touched-off the series of events that led to my thesis so a great credit is due to her. You can find her thesis here (http://dspace.mit.edu/handle/1721.1/72870).
3) Since the thesis of A. Webb, there were two key developments that made the high resolution of the current maps believable as a good approximation of the actual thermal environment of a building. The first is a PhD thesis by Alejandra Menchaca (also conducted here at MIT) that developed a computationally fast way of estimating sub-zone air temperature stratification. The method, which works simply by weighing the heat gain in a room against the incoming airflow was validated by many CFD simulations over the course of Alejandra's thesis. You can find here final thesis document here (http://dspace.mit.edu/handle/1721.1/74907).
4) The other main development since the A. Webb thesis that made the radiant map much more accurate is a fast means of estimating the radiant temperature increase felt by an occupant sitting in the sun. This method was developed by some awesome scientists at the UC Berkeley Center for the Built Environment (CBE) Including Tyler Hoyt, who has been particularly helpful to me by supporting the CBE's Github page. The original paper on this fast means of estimating the solar temperature delta can be found here (http://escholarship.org/uc/item/89m1h2dg) although they should have an official publication in a journal soon.
5) The ASHRAE comfort models under the hood of LB+HB all are derived from the javascript of the CBE comfort tool (http://smap.cbe.berkeley.edu/comforttool). A huge chunk of credit definitely goes to this group and I encourage any other researchers who are getting deep into comfort to check the code resources on their github page (https://github.com/CenterForTheBuiltEnvironment/comfort_tool).
6) And, last but not least, a huge share of credit is due to Mostapha and all members of the LB+HB community. It is because of resources and help that Mostapha initially gave me that I learned how to code in the first place and the knowledge of a community that would use the things that I developed was, by fa,r the biggest motivation throughout this thesis and all of my LB efforts.
Thank you all and stay awesome,
-Chris…
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Thanx……
curve or locus] of a segment AB, in English. The set of all the points from which a segment, AB, is seen under a fixed given angle.
When you construct l'arc capable —by using compass— you obviously need to find the centre of this arc. This can be easily done in GH in many ways by using some trigonometry (e.g. see previous —great— solutions). Whole circles instead of arcs provide supplementary isoptics —β-isoptic and (180º-β)-isoptic—. Coherent normals let you work in any plane.
Or you could just construct β-isoptics of AB by using tangent at A (or B). I mean [Arc SED] component.
If you want the true β-isoptic —the set of all the points— you should use {+β, -β} degrees (2 sides; 2 solutions; 2 arcs), but slider in [-180, +180] degrees provides full range of signed solutions. Orthoptic is provided by ±90º. Notice that ±180º isoptic is just AB segment itself, and 0º isoptic should be the segment outside AB —(-∞, A] U [B, +∞)—. [Radians] component is avoidable.
More compact versions can be achieved by using [F3] component. You can choose among different expressions the one you like the most as long as performs counter clockwise rotation of vector AB, by 180-β degrees, around A; or equivalent. [Panel] is totally avoidable.
Solutions in XY plane —projection; z = 0—, no matter A or B, are easy too. Just be sure about the curve you want to find the intersection with —Curve; your wall— being contained in XY plane.
A few self-explanatory examples showing features.
1 & 5 1st ver. (Supplementary isoptics) (ArcCapableTrigNormals_def_Bel.png)
2 & 6 2nd ver. (SED) (ArcCapableSED_def_Bel.png)
3 & 7 3rd ver. (SED + F3) (ArcCapableSEDF3_def_Bel.png)
4 & 8 4th ver. (SED + F3, Projection) (ArcCapableSEDProjInt_def_Bel.png)
If you want to be compact, 7 could be your best choice. If you prefer orientation robustness, 5. Etcetera.
I hope these versions will help you to compact/visualize; let me know any feedback.
Calculate where 2 points [A & B] meet at a specific angle is just find the geometrical locus called arco capaz in Spanish, arc capable in French (l'isoptique d'un segment de droite) or isoptic [curve or locus]
of a segment AB, in English. The set of all the points from which a segment,
AB, is seen under a fixed given angle.…
lly it should not make much of a difference - random number generation is not affected, mutation also is not. crossover is a bit more tricky, I use Simulated Binary Crossover (SBX-20) which was introduced already in 1194:
Deb K., Agrawal R. B.: Simulated Binary Crossover for Continuous Search Space, inIITK/ME/SMD-94027, Convenor, Technical Reports, Indian Institue of Technology, Kanpur, India,November 1994
Abst ract. The success of binary-coded gene t ic algorithms (GA s) inproblems having discrete sear ch sp ace largely depends on the codingused to represent the prob lem variables and on the crossover ope ratorthat propagates buildin g blocks from pare nt strings to childrenst rings . In solving optimization problems having continuous searchspace, binary-co ded GAs discr et ize the search space by using a codingof the problem var iables in binary st rings. However , t he coding of realvaluedvari ables in finit e-length st rings causes a number of difficulties:inability to achieve arbit rary pr ecision in the obtained solution , fixedmapping of problem var iab les, inh eren t Hamming cliff problem associatedwit h binary coding, and processing of Holland 's schemata incont inuous search space. Although a number of real-coded GAs aredevelop ed to solve optimization problems having a cont inuous searchspace, the search powers of these crossover operators are not adequate .In t his paper , t he search power of a crossover operator is defined int erms of the probability of creating an arbitrary child solut ion froma given pair of parent solutions . Motivated by t he success of binarycodedGAs in discret e search space problems , we develop a real-codedcrossover (which we call the simulated binar y crossover , or SBX) operatorwhose search power is similar to that of the single-point crossoverused in binary-coded GAs . Simulation results on a number of realvaluedt est problems of varying difficulty and dimensionality suggestt hat the real-cod ed GAs with t he SBX operator ar e ab le to perform asgood or bet t er than binary-cod ed GAs wit h t he single-po int crossover.SBX is found to be particularly useful in problems having mult ip le optimalsolutions with a narrow global basin an d in prob lems where thelower and upper bo unds of the global optimum are not known a priori.Further , a simulation on a two-var iable blocked function showsthat the real-coded GA with SBX work s as suggested by Goldberg
and in most cases t he performance of real-coded GA with SBX is similarto that of binary GAs with a single-point crossover. Based onth ese encouraging results, this paper suggests a number of extensionsto the present study.
7. ConclusionsIn this paper, a real-coded crossover operator has been develop ed bas ed ont he search characte rist ics of a single-point crossover used in binary -codedGAs. In ord er to define the search power of a crossover operator, a spreadfactor has been introduced as the ratio of the absolute differences of thechildren points to that of the parent points. Thereaft er , the probabilityof creat ing a child point for two given parent points has been derived forthe single-point crossover. Motivat ed by the success of binary-coded GAsin problems wit h discrete sear ch space, a simul ated bin ary crossover (SBX)operator has been develop ed to solve problems having cont inuous searchspace. The SBX operator has search power similar to that of the single-po intcrossover.On a number of t est fun ctions, including De Jong's five te st fun ct ions, ithas been found that real-coded GAs with the SBX operator can overcome anumb er of difficult ies inherent with binary-coded GAs in solving cont inuoussearch space problems-Hamming cliff problem, arbitrary pr ecision problem,and fixed mapped coding problem. In the comparison of real-coded GAs wit ha SBX operator and binary-coded GAs with a single-point crossover ope rat or ,it has been observed that the performance of the former is better than thelatt er on continuous functions and the performance of the former is similarto the lat ter in solving discret e and difficult functions. In comparison withanother real-coded crossover operator (i.e. , BLX-0 .5) suggested elsewhere ,SBX performs better in difficult test functions. It has also been observedthat SBX is particularly useful in problems where the bounds of the optimum
point is not known a priori and wher e there are multi ple optima, of whichone is global.Real-coded GAs wit h t he SBX op erator have also been tried in solvinga two-variab le blocked function (the concept of blocked fun ctions was introducedin [10]). Blocked fun ct ions are difficult for real-coded GAs , becauselocal optimal points block t he progress of search to continue towards t heglobal optimal point . The simulat ion results on t he two-var iable blockedfunction have shown that in most occasions , the sea rch proceeds the way aspr edicted in [10]. Most importantly, it has been observed that the real-codedGAs wit h SBX work similar to that of t he binary-coded GAs wit h single-pointcrossover in overcoming t he barrier of the local peaks and converging to t heglobal bas in. However , it is premature to conclude whether real-coded GAswit h SBX op erator can overcome t he local barriers in higher-dimensionalblocked fun ct ions.These results are encour aging and suggest avenues for further research.Because the SBX ope rat or uses a probability distribut ion for choosing a childpo int , the real-coded GAs wit h SBX are one st ep ahead of the binary-codedGAs in te rms of ach ieving a convergence proof for GAs. With a direct probabilist ic relationship between children and parent points used in t his paper,cues from t he clas sical stochast ic optimization methods can be borrowed toachieve a convergence proof of GAs , or a much closer tie between the classicaloptimization methods and GAs is on t he horizon.
In short, according to the authors my SBX operator using real gene values is as good as older ones specially designed for discrete searches, and better in continuous searches. SBX as far as i know meanwhile is a standard general crossover operator.
But:
- there might be better ones out there i just havent seen yet. please tell me.
- besides tournament selection and mutation, crossover is just one part of the breeding pipeline. also there is the elite management for MOEA which is AT LEAST as important as the breeding itself.
- depending on the problem, there are almost always better specific ways of how to code the mutation and the crossover operators. but octopus is meant to keep it general for the moment - maybe there's a way for an interface to code those things yourself..!?
2) elite size = SPEA-2 archive size, yes. the rate depends on your convergence behaviour i would say. i usually start off with at least half the size of the population, but mostly the same size (as it is hard-coded in the new version, i just realize) is big enough.
4) the non-dominated front is always put into the archive first. if the archive size is exceeded, the least important individual (the significant strategy in SPEA-2) are truncated one by one until the size is reached. if it is smaller, the fittest dominated individuals are put into the elite. the latter happens in the beginning of the run, when the front wasn't discovered well yet.
3) yes it is. this is a custom implementation i figured out myself. however i'm close to have the HypE algorithm working in the new version, which natively has got the possibility to articulate perference relations on sets of solutions.
…
ager As Grasshopper.Kernel.GH_Component.GH_InputParamManager)
pManager.AddTextParameter(
"Name", "N", "String", GH_ParamAccess.item)
pManager.AddPointParameter(
"Point", "P", "Point3d", GH_ParamAccess.item)
pManager.AddGenericParameter(
"Local Axis", "LA", "Null/Surface/Plane", GH_ParamAccess.item)
pManager.AddGenericParameter(
"Support", "S", "I_Model_Support", GH_ParamAccess.item)
pManager.AddGenericParameter(
"PointLoad", "PL", "List (of I_Model_PointLoad)", GH_ParamAccess.list)
pManager.AddGenericParameter(
"Group", "G", "List (of (I_Model_Group)", GH_ParamAccess.list)
End Sub
Protected Overrides Sub RegisterOutputParams(ByVal pManager As Grasshopper.Kernel.GH_Component.GH_OutputParamManager)
pManager.AddGenericParameter(
"Node", "N", "I_Model_Node",GH_ParamAccess.item)
End Sub
Protected Overrides Sub SolveInstance(ByVal DA As Grasshopper.Kernel.IGH_DataAccess)
Dim inName As String = Nothing
Dim inP As Point3d = Nothing
Dim inLA As Plane = Nothing
Dim inS As I_Model.I_Model_NodeSupport = Nothing
Dim inPL As New List(Of I_Model.I_Model_PointLoad)
Dim inG As New List(Of I_Model.I_Model_Group)
Dim outNode As I_Model.I_Model_Node
If Not DA.GetData(0, inName) Then Return
If Not DA.GetData(1, inP) Then Return
If Not DA.GetData(2, inLA) Then Return
If Not DA.GetData(3, inS) Then Return
If Not DA.GetData(4, inPL) Then Return
If Not DA.GetData(5, inG) Then Return
Dim IM_P As I_Model_Node
IM_P =
New I_Model_Node(inP, Nothing, inName)
If Not DA.GetData(2, inLA) Then IM_P.SetLocalAxis(inLA)
If Not DA.GetData(3, inS) Then IM_P.SetSupport(inS)
If Not DA.GetData(4, inPL) Then
Dim PL As I_Model_PointLoad
For Each PL In inPL
IM_P.AddPointLoad(PL)
Next
End If
If Not DA.GetData(5, inG) Then
Dim G As I_Model_Group
For Each G In inG
IM_P.AddToGroup(G)
Next
End If
outNode = IM_P
DA.SetData(0, outNode)
End Sub
…
Added by Daniel Bosia at 9:22am on January 11, 2013
This blog post is a rough approximation of the lecture I gave at the AAG10 conference in Vienna on September 21st 2010. Naturally it will be quite a different experience as the medium is quite…
Added by David Rutten at 3:27pm on September 24, 2010