August 2016/Graphene - Revision history

Admin: /* Chart 1: Species Reaction Rates */

2016-08-02T01:26:16Z

Chart 1: Species Reaction Rates

← Older revision		Revision as of 01:26, 2 August 2016
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	==Chart 1: Species Reaction Rates==		==Chart 1: Species Reaction Rates==

	D3 chart of ranking of chemical species by reaction ~~rate~~		For our first chart, we will make a D3 bar chart that shows a ranking of chemical species reaction rates.

	Plot 1A (warm up): generation		Plot 1A (warm up): generation
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	* right side, generation rate bars		* right side, generation rate bars
	* ranked by: magnitude of gen/con rates		* ranked by: magnitude of gen/con rates



	=Notes=		=Notes=

Admin at 01:24, 2 August 2016

2016-08-02T01:24:03Z

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			=On The Stack=

			==Chart 1: Species Reaction Rates==

			D3 chart of ranking of chemical species by reaction rate

			Plot 1A (warm up): generation
			* Bar chart, horizontal
			* Magnitude of species generation term, ranked in order
			* Labels on left, bars on right

			Plot 1B (warm up): consumption
			* Bar chart, horizontal
			* Magnitude of species consumption term, ranked in order
			* Labels on RIGHT, bars on LEFT

			Plot 1C (1st real plot): net rates
			* Bar chart, split and on both sides
			* left side: consumption rate bars
			* right side, generation rate bars
			* ranked by: magnitude of gen/con rates



	=Notes=		=Notes=

Admin: /* Python step */

2016-08-02T00:24:03Z

Python step

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	As mentioned above, the Cantera portion of Graphene is intended to run a simulation and save the state of the gas at periodic intervals. It is up to the post-processing script to decide what data is important, what relationships to create, and so on. This allows us to be as flexible as possible with the D3 visualizations.		As mentioned above, the Cantera portion of Graphene is intended to run a simulation and save the state of the gas at periodic intervals. It is up to the post-processing script to decide what data is important, what relationships to create, and so on. This allows us to be as flexible as possible with the D3 visualizations.

			Python is loading the thermochemical state of the gas, then creating a new gas object with that same thermochemical state. This gas object can then be queried to find out lots of interesting information. Some quantities we may be interested in:
			* Species concentrations (linear/log)
			* Species net/gross generation/consumption rates
			* Chemical reactions forward/reverse/net reaction rates
			* Chemical reactions kinetic rate constants
			* Chemical reactions largest energy release
			* Chemical reactions largest heat release rates

			Energetics: a very small, fixed timestep has some associated amount of heat released. Where does this heat come from? ("It comes from reactions ABC. It comes from the consumption of XYZ.")

			Mass: a very small, fixed timestep has zero mass change. But the balance of species changes. What are the various concentrations, and how quickly are they changing?

	=Flags=		=Flags=

Admin: /* Cantera output= */

2016-08-02T00:12:57Z

Cantera output=

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	(Also remember: OCM/Pantera was designed for an entirely different purpose - and will definitely be overkill for Graphene.)		(Also remember: OCM/Pantera was designed for an entirely different purpose - and will definitely be overkill for Graphene.)

	===Cantera output====		===Cantera output===

	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. The state of the gas can be reduced to a JSON object or a single string containing TPX (temperature, pressure, composition). These completely specify the thermochemical state of the gas, and any other quantity can be obtained from there.		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. The state of the gas can be reduced to a JSON object or a single string containing TPX (temperature, pressure, composition). These completely specify the thermochemical state of the gas, and any other quantity can be obtained from there.
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	The purpose of "bottling" the gas and saving that information is that it uses the absolute minimal storage space necessary. In the Python post-processing script, each gas can be "unbottled" and various properties tabulated - entropy, generation or consumption rates, reaction rate constants, and so on.		The purpose of "bottling" the gas and saving that information is that it uses the absolute minimal storage space necessary. In the Python post-processing script, each gas can be "unbottled" and various properties tabulated - entropy, generation or consumption rates, reaction rate constants, and so on.

	====Python step====		===Python step===

	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.)		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.)

Admin: /* Notes on using Cantera */

2016-08-02T00:12:47Z

Notes on using Cantera

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	Since we are restarting Graphene, we will either have to do away with all of that, and go back to caveman style, or to re-implement everything into compact objects specific to Graphene, or to use Pantera, which would require more dusting off of more code and potentially opening more cans of worms.		Since we are restarting Graphene, we will either have to do away with all of that, and go back to caveman style, or to re-implement everything into compact objects specific to Graphene, or to use Pantera, which would require more dusting off of more code and potentially opening more cans of worms.

	~~Fortunately for us,~~ Pantera ~~isn't going anywhere, and right~~ now ~~we want~~ to focus on D3. ~~So let~~'s ~~get some~~ data ~~generated~~ first.		The goal here is not to focus on Cantera or Pantera (maybe later). Right now the goal is to focus on D3. That means we need to get whatever Cantera code we need written and out of the way as soon as possible. That means charging through it. Let's focus on generating a single data set and visualizing a single reaction network, first, and then we can worry about making the output fancy.

			(Also remember: OCM/Pantera was designed for an entirely different purpose - and will definitely be overkill for Graphene.)

	===Cantera output====		===Cantera output====

Admin: /* Uses of graphene */

2016-08-02T00:11:06Z

Uses of graphene

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 </pre>
-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.
+===Notes on using Cantera===
 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.)
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Admin: /* Uses of graphene */

2016-08-01T23:54:38Z

Uses of graphene

← Older revision		Revision as of 23:54, 1 August 2016
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	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.)		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.)

			=Flags=

			[[Category:Cantera]]

			[[Category:Graphene]]

			[[Category:August 2016]]

Admin: /* Monday August 1 */

2016-08-01T23:54:16Z

Monday August 1

← Older revision		Revision as of 23:54, 1 August 2016
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	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.		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?		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?
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	* 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."		* 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."		* 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."		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."
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	\|____________\| \|__________________\| \|_________\| \|___________________\| \|___________\|		\|____________\| \|__________________\| \|_________\| \|___________________\| \|___________\|
	</pre>		</pre>

			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.)

Admin: /* Monday August 1 */

2016-08-01T23:50:53Z

Monday August 1

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 - - - -                                  - - - -
 - - - -                                  - - - -
 </pre>

Admin: /* Monday August 1 */

2016-08-01T23:45:35Z

Monday August 1

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 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?
-Because it's complicated and nonlinear and stiff and extremely sensitive to small variations.
+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:
+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"?
 <math>
@@ Line 25: / Line 25: @@
 </math>
-What makes this reaction easier to "understand"?
+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: