August 2016/Graphene
From charlesreid1
Notes
Monday August 1
If graphene as is isn't working... change the approach. Don't give up on Graphene.
Come up with a few ideas - ways of visualizing
Pick the simplest and go from there
After: go to literature, search out reaction pathways and mechanisms, verify your findings/visualization methods
A word on data vs. simulation: only visualizing simulation data - can't draw real conclusions, and only stating what the model assumptions imply. Like a thought experiment, incorrect assumptions lead to incorrect results.
With Graphene... what is it we're really trying to see? We're trying to understand a mechanism, which is like a giant Rube Goldberg machine, and how given inputs and a given starting state will create a chemical cascade into the output products.
Clarification questions
Q: to understand a mechanism... what is it we're trying to understand? Why can't we just print out everything we're interested in, and parse through the numbers ourselves?
A: Because it's complicated and nonlinear and stiff and extremely sensitive to small variations.
Q: okay, let's say we're dealing with a simpler reaction: for example, ammonia synthesis. Let's consider the Haber Process, in particular: what makes this reaction easier to "understand"?
$ N_2 + 3 H_2 (g) \leftrightarrow 2 NH_3 $
A: Well, the answer is in the Fogler textbook - a basic course in undergraduate reactor design/reaction engineering. But in short, a simple reaction network allows you to:
- Easily understand the affect of various inputs like temperature or reactant feed ratio
- Construct both analytical (mathematical) and mental (intuitive) models of the process, understand the broader context of how rxn changes in thermochemical state space
- If we want to know, "What happens when T goes up 20 degrees?," with a simple rxn network we can point to the mechanism and say, this changes that, that changes this, XYZ. But with a complex rxn network we shrug and say, I dunno, let's find out.
- Where is the energy going, where is the mass going, what are the pathways, when and where and why do they open or close?
- If you can get a better intuitive understanding of the reaction network, you can gain a deeper understanding of cause and effect: "T increased 20 degrees here, that shuts off the flow to this radical pool and opens up this other radical pool, which interferes with these pathways, and turns on these side reactions."
- Or: "Here's what your gas looks like when it hits the catalyst at the top of the bed." "Use that condition to feed into the next condition, and you can see that those radicals that aren't available at the top, are available here, and that changes these pathways."
Uses of graphene
More about the uses of graphene: Ideally, we would be able to identify, under certain conditions, for example, "reactants follow these two pathways, which interact in this certain way. When you increase X, it shuts off Y, causing Z."
Then, you can figure out, Path 1 is XYZ, Path 2 is JKL, so now we can look at a different but related condition, and see what affect that change has.
I see a big potential issue: this network is an absolute SPIDER WEB. This raises the question of what I'm actually trying to do, and what filtering/processing will happen where in the process.
In reality, there may be dozens of pathways, connected by hundreds of side-paths. So the question is, what dominates?
Picking which species or which reactions to display requires defining a threshhold.
Full Species List Full Rxn List - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Reduced Species List Reduced Rxn List - - - - - - - - - - - - - - - -
Starting point:
- Ranked species list
- Ranked reaction list
- Use ranked list to construct a reduced graph
This raises more questions: how is this different from what I was doing before? Where does the processing happen - preprocessing, or with D3?
Avoid processing with D3 as much as possible. So, add an extra step between Cantera and D3: post-Cantera, pre-D3 processing of the reaction data with Python.
This is important, since the graphs can be very unwieldy. Want to do as much data reduction as possible with Python.
______________ ____________________ ___________ _____________________ _____________ | | | | | | | | | | |Cantera | | Cantera output | | Python | | Graphene input | | Graphene | |(full info) |----->| (reduced output) |----->| (magic) |----->| (processed input) |----->| | | | | | | | | | | | |____________| |__________________| |_________| |___________________| |___________|
The Cantera code would be running a batch reactor, and stepping through the differential equations, one step at a time. (Think autoignition, to start.) At each step, or at specified time intervals, we bottle up the gas and save the gas state. That's it - no other processing or saving or anything like that.
Next step is Python post-processing. Generally speaking, one Python post-processing script corresponds to one visualization in Graphene - Python script will create custom output for a particular type of D3 plot, in whatever structure it requires. (Remember: as little processing in D3 as possible.)