I'm trying to model the flow and suspension of microcarriers (particles that are used as surfaces for cells to attach to and grow on) in a CFD application. I know some basic characteristics of the particles (they're called "Cytodex", about 180 µm big, density is 1.03g/cm^2) but I'd like to find the Stokes number to determine how strongly they are affected by turbulence and movement of the fluid. Can somebody point me to how to approach this (or at least approximate?). It's surprisingly hard to find any information for somebody like me who hasn't got a very strong background in fluid mechanics.
Here is the manufacturer's microcarrier manual. See page 62, Table 12 for Cytodex 1 physical properties.
https://www.gelifesciences.co.kr/wp-content/uploads/2016/07/023.8_Microcarrier-Cell-Culture.pdf
See this SlideShare, slide 15, for how to calculate the Stokes # for Cytodex 1 microcarriers: https://www.slideshare.net/rjrishabhjain/bs-4sedimentation?from_action=save
but for Cytodex 1 correct the d=180 um, for cell culture media=nutrient broth viscosity = 0.96 cP, media density ~ 1.007g/mL, microcarrier density 1.03 g/mL, to get settling velocity of 0.062cm/s = 3.72 cm/min. However, per the manufacturer's manual settling velocity is 12-16mL/min. Might be an error. I am seeing an answer.
For CFD modeling of microcarriers in bioreactors see: Loubière
https://pdfs.semanticscholar.org/d955/75b5c640c8268fd1ec51b2ce46862e7bbfbd.pdf
I have more related literature if you have interest.
DApple
Using pyiron, I want to calculate the mean square displacement of the ions in my system. How do I see the total displacement (i.e. not folded back by periodic boundary conditions) without dumping very frequently and checking when an atom passes over the boundary and gets wrapped?
Try to compare job['output/generic/unwrapped_positions'][-1] and job.structure.positions+job.output.total_displacements[-1]. If they deliver the same values, it's definitely fine both ways. If not, you can post the relevant lines in your notebook here.
I'd like to add a few comments to Jan's answer:
While job['output/generic/unwrapped_positions'] returns the unwrapped positions parsed from the output files, job.output.total_displacements returns the displacement of atoms calculated from each pair of consecutive snapshots. So if an atom moves more than half the box length in any direction, job.output.total_displacements will give wrong coordinates. Therefore, job['output/generic/unwrapped_positions'] is generally more trustworthy, but it is not available in all the codes (since some codes simply do not provide an output for unwrapped positions).
Moreover, if an interactive job is used, it is possible that job.structure.positions does not return the initial positions, i.e. job.structure.positions+job.output.total_displacements won't be initial positions + displacements.
So, in short, my answer to your question would be rather "Use job['output/generic/unwrapped_positions'] and if it's not available, use job.structure.positions+job.output.total_displacements but be aware of potential problems you might be running into."
I have a set of points on an OPEN surface in 3D space.
I have identified a subset of points which lay on the boundary.
I mean to generate a triangulation of those points, which gives me an open surface and keeps my selected points on the boundary.
All references I found deal with (sometimes?) closed surfaces, e.g., CGAL.
See examples below.
In addition, some CGAL algorithms require oriented normals at each point, which I do not have.
Is there an available algorithm and code for this? (either CGAL Advancing_front_surface_reconstruction, properly handled, or any other)
See also this and this.
Example 1
I compiled and ran example reconstruction_surface_mesh.cpp from examples/Advancing_front_surface_reconstruction, out-of-the box (which uses file half.xyz as input for data points), and I obtained a closed surface:
I would like to get rid of the few triangles that close the surface.
I tried adding an extra point at the end of half.xyz, and I got
which is an open surface.
So far, with what I tested, I do not know:
How to indicate an open surface.
How to indicate which vertices lay at the boundary.
If this is a non-empty set (and it should have at least three vertices) this would imply an open surface.
Ideally, one would have a workflow which works without manual intervention.
Example 2
I compiled and ran example boundaries.cpp, out-of-the box (which also uses file half.xyz as input for data points).
The output is:
0 outliers:
Boundaries:
boundary
0.178269 0.438589 0.129521
0.0795598 0.419465 0.244812
0.0549683 0.377617 0.3119
-0.0295721 0.360972 0.329075
-0.111332 0.334417 0.342617
-0.186667 0.2953 0.346683
-0.2719 0.16555 0.375017
-0.336304 0.117058 0.339323
-0.393517 0.0775 0.285917
-0.421419 -0.126854 0.215271
-0.395217 -0.214417 0.20015
-0.354783 -0.2953 0.170767
-0.237067 -0.395867 0.172233
-0.178246 -0.438588 0.129553
0.0227767 -0.4873 0.0700833
0.220338 -0.438589 -7.23321e-06
0.293 -0.395867 0
0.36025 -0.334417 0
0.418077 -0.258382 6.0303e-05
0.46025 -0.17265 0
0.484417 -0.0425167 -0.0763333
0.485067 0.03875 -0.0782667
0.471547 0.117058 -0.076827
0.44605 0.197567 -0.0700833
0.4092 0.27125 -0.0433167
0.364885 0.329645 0
0.313633 0.377617 0.0441167
0.2509 0.41425 0.0879333
I did not find how to use this for
automatically removing triangles which would make my target boundary vertices not laying at the boundary.
Moreover, the output seems to be the list of boundary points, without the "spurious" triangles (I am not sure). I would like to specify this list.
The CGAL advancing front reconstruction algorithm does generate open surfaces in general.
I iterate over finie_vertieces, finite_edges and finite_faces after generating constrained delauny triangulation with Loyd optimization. I am on VS2012 using CGAL 4.12 under release mode. I see for a given case finite_verices list is repeatable (so is the vertex list under finite_faces), however, the ordering of the edges in finite_edges seems to change from run to run
for(auto eit = cdtp.finite_edges_begin(); eit != cdtp.finite_edges_end(); ++eit)
{
const auto isConstrainedEdge = cdtp.is_constrained(*eit);
auto & cFace = *(eit->first);
auto cwVert = cFace.vertex(cFace.cw(eit->second));
auto ccwVert = cFace.vertex(cFace.ccw(eit->second));
I use the above code snippet to extract vertex list, and vertex list with a given edge changes from run to run.
Any help is appreciated resolving this, as I am looking for consistent behavior in the code. My triangulation involves many line constraints on a two dimensional domain.
I was told it's likely dependable behaviour, but there is no guarantee of order. IIRC the documentation says the traversal order is not guaranteed. I think it's best to assume the iterators' transversal is not deterministic and could change.
You could use any of the _info extensions to embed information into the face, edge, etc (a hash perhaps?) which you could then check against to detect a change.
In my use case, I wanted to traverse the mesh in parallel and OpenMP didn't support the iterators. So I hold a vector of the Face_handles in memory which I can then easily index over. In conjunction with the _info data, you could use this to build a vector of edges,faces, etc with a guaranteed order using unique information in the ->info() field.
Another _info example.
I'm not talking about algorithmic stuff (eg use quicksort instead of bubblesort), and I'm not talking about simple things like loop unrolling.
I'm talking about the hardcore stuff. Like Tiny Teensy ELF, The Story of Mel; practically everything in the demoscene, and so on.
I once wrote a brute force RC5 key search that processed two keys at a time, the first key used the integer pipeline, the second key used the SSE pipelines and the two were interleaved at the instruction level. This was then coupled with a supervisor program that ran an instance of the code on each core in the system. In total, the code ran about 25 times faster than a naive C version.
In one (here unnamed) video game engine I worked with, they had rewritten the model-export tool (the thing that turns a Maya mesh into something the game loads) so that instead of just emitting data, it would actually emit the exact stream of microinstructions that would be necessary to render that particular model. It used a genetic algorithm to find the one that would run in the minimum number of cycles. That is to say, the data format for a given model was actually a perfectly-optimized subroutine for rendering just that model. So, drawing a mesh to the screen meant loading it into memory and branching into it.
(This wasn't for a PC, but for a console that had a vector unit separate and parallel to the CPU.)
In the early days of DOS when we used floppy discs for all data transport there were viruses as well. One common way for viruses to infect different computers was to copy a virus bootloader into the bootsector of an inserted floppydisc. When the user inserted the floppydisc into another computer and rebooted without remembering to remove the floppy, the virus was run and infected the harddrive bootsector, thus permanently infecting the host PC. A particulary annoying virus I was infected by was called "Form", to battle this I wrote a custom floppy bootsector that had the following features:
Validate the bootsector of the host harddrive and make sure it was not infected.
Validate the floppy bootsector and
make sure that it was not infected.
Code to remove the virus from the
harddrive if it was infected.
Code to duplicate the antivirus
bootsector to another floppy if a
special key was pressed.
Code to boot the harddrive if all was
well, and no infections was found.
This was done in the program space of a bootsector, about 440 bytes :)
The biggest problem for my mates was the very cryptic messages displayed because I needed all the space for code. It was like "FFVD RM?", which meant "FindForm Virus Detected, Remove?"
I was quite happy with that piece of code. The optimization was program size, not speed. Two quite different optimizations in assembly.
My favorite is the floating point inverse square root via integer operations. This is a cool little hack on how floating point values are stored and can execute faster (even doing a 1/result is faster than the stock-standard square root function) or produce more accurate results than the standard methods.
In c/c++ the code is: (sourced from Wikipedia)
float InvSqrt (float x)
{
float xhalf = 0.5f*x;
int i = *(int*)&x;
i = 0x5f3759df - (i>>1); // Now this is what you call a real magic number
x = *(float*)&i;
x = x*(1.5f - xhalf*x*x);
return x;
}
A Very Biological Optimisation
Quick background: Triplets of DNA nucleotides (A, C, G and T) encode amino acids, which are joined into proteins, which are what make up most of most living things.
Ordinarily, each different protein requires a separate sequence of DNA triplets (its "gene") to encode its amino acids -- so e.g. 3 proteins of lengths 30, 40, and 50 would require 90 + 120 + 150 = 360 nucleotides in total. However, in viruses, space is at a premium -- so some viruses overlap the DNA sequences for different genes, using the fact that there are 6 possible "reading frames" to use for DNA-to-protein translation (namely starting from a position that is divisible by 3; from a position that divides 3 with remainder 1; or from a position that divides 3 with remainder 2; and the same again, but reading the sequence in reverse.)
For comparison: Try writing an x86 assembly language program where the 300-byte function doFoo() begins at offset 0x1000... and another 200-byte function doBar() starts at offset 0x1001! (I propose a name for this competition: Are you smarter than Hepatitis B?)
That's hardcore space optimisation!
UPDATE: Links to further info:
Reading Frames on Wikipedia suggests Hepatitis B and "Barley Yellow Dwarf" virus (a plant virus) both overlap reading frames.
Hepatitis B genome info on Wikipedia. Seems that different reading-frame subunits produce different variations of a surface protein.
Or you could google for "overlapping reading frames"
Seems this can even happen in mammals! Extensively overlapping reading frames in a second mammalian gene is a 2001 scientific paper by Marilyn Kozak that talks about a "second" gene in rat with "extensive overlapping reading frames". (This is quite surprising as mammals have a genome structure that provides ample room for separate genes for separate proteins.) Haven't read beyond the abstract myself.
I wrote a tile-based game engine for the Apple IIgs in 65816 assembly language a few years ago. This was a fairly slow machine and programming "on the metal" is a virtual requirement for coaxing out acceptable performance.
In order to quickly update the graphics screen one has to map the stack to the screen in order to use some special instructions that allow one to update 4 screen pixels in only 5 machine cycles. This is nothing particularly fantastic and is described in detail in IIgs Tech Note #70. The hard-core bit was how I had to organize the code to make it flexible enough to be a general-purpose library while still maintaining maximum speed.
I decomposed the graphics screen into scan lines and created a 246 byte code buffer to insert the specialized 65816 opcodes. The 246 bytes are needed because each scan line of the graphics screen is 80 words wide and 1 additional word is required on each end for smooth scrolling. The Push Effective Address (PEA) instruction takes up 3 bytes, so 3 * (80 + 1 + 1) = 246 bytes.
The graphics screen is rendered by jumping to an address within the 246 byte code buffer that corresponds to the right edge of the screen and patching in a BRanch Always (BRA) instruction into the code at the word immediately following the left-most word. The BRA instruction takes a signed 8-bit offset as its argument, so it just barely has the range to jump out of the code buffer.
Even this isn't too terribly difficult, but the real hard-core optimization comes in here. My graphics engine actually supported two independent background layers and animated tiles by using different 3-byte code sequences depending on the mode:
Background 1 uses a Push Effective Address (PEA) instruction
Background 2 uses a Load Indirect Indexed (LDA ($00),y) instruction followed by a push (PHA)
Animated tiles use a Load Direct Page Indexed (LDA $00,x) instruction followed by a push (PHA)
The critical restriction is that both of the 65816 registers (X and Y) are used to reference data and cannot be modified. Further the direct page register (D) is set based on the origin of the second background and cannot be changed; the data bank register is set to the data bank that holds pixel data for the second background and cannot be changed; the stack pointer (S) is mapped to graphics screen, so there is no possibility of jumping to a subroutine and returning.
Given these restrictions, I had the need to quickly handle cases where a word that is about to be pushed onto the stack is mixed, i.e. half comes from Background 1 and half from Background 2. My solution was to trade memory for speed. Because all of the normal registers were in use, I only had the Program Counter (PC) register to work with. My solution was the following:
Define a code fragment to do the blend in the same 64K program bank as the code buffer
Create a copy of this code for each of the 82 words
There is a 1-1 correspondence, so the return from the code fragment can be a hard-coded address
Done! We have a hard-coded subroutine that does not affect the CPU registers.
Here is the actual code fragments
code_buff: PEA $0000 ; rightmost word (16-bits = 4 pixels)
PEA $0000 ; background 1
PEA $0000 ; background 1
PEA $0000 ; background 1
LDA (72),y ; background 2
PHA
LDA (70),y ; background 2
PHA
JMP word_68 ; mix the data
word_68_rtn: PEA $0000 ; more background 1
...
PEA $0000
BRA *+40 ; patched exit code
...
word_68: LDA (68),y ; load data for background 2
AND #$00FF ; mask
ORA #$AB00 ; blend with data from background 1
PHA
JMP word_68_rtn ; jump back
word_66: LDA (66),y
...
The end result was a near-optimal blitter that has minimal overhead and cranks out more than 15 frames per second at 320x200 on a 2.5 MHz CPU with a 1 MB/s memory bus.
Michael Abrash's "Zen of Assembly Language" had some nifty stuff, though I admit I don't recall specifics off the top of my head.
Actually it seems like everything Abrash wrote had some nifty optimization stuff in it.
The Stalin Scheme compiler is pretty crazy in that aspect.
I once saw a switch statement with a lot of empty cases, a comment at the head of the switch said something along the lines of:
Added case statements that are never hit because the compiler only turns the switch into a jump-table if there are more than N cases
I forget what N was. This was in the source code for Windows that was leaked in 2004.
I've gone to the Intel (or AMD) architecture references to see what instructions there are. movsx - move with sign extension is awesome for moving little signed values into big spaces, for example, in one instruction.
Likewise, if you know you only use 16-bit values, but you can access all of EAX, EBX, ECX, EDX , etc- then you have 8 very fast locations for values - just rotate the registers by 16 bits to access the other values.
The EFF DES cracker, which used custom-built hardware to generate candidate keys (the hardware they made could prove a key isn't the solution, but could not prove a key was the solution) which were then tested with a more conventional code.
The FSG 2.0 packer made by a Polish team, specifically made for packing executables made with assembly. If packing assembly isn't impressive enough (what's supposed to be almost as low as possible) the loader it comes with is 158 bytes and fully functional. If you try packing any assembly made .exe with something like UPX, it will throw a NotCompressableException at you ;)