Searching for data through twitter proved to be very difficult and tedious. Each day we ran up to 35 queries through the twitter search API. For those of you who aren't familiar with twitter queries, here is an example of one from our data set.
q=%40chewy&count=100
The first term after the "q=" is the search term. In this case, we are searching for any tweets containing the phrase "@chewy" - %40 is the ASCII Hex symbol for the @ sign. Since the default number of tweets per query is 15, we set this count to 100 to minimize the amount of data we missed. In other queries, we added an additional characteristic of result type, which allows you to search for the most popular tweets that match that query, the most recent tweets that match that query, or a mixture of both. Since some of our search terms were very popular, we didn't want to get stuck with just tweets posted within the past hour, and we also didn't only want the most popular tweets. So for our more popular terms, we added a component of "&result_type=mixed" to our queries.
We used the curl command generated for each query to get our data in the form of a .txt file. Since the .txt file was in a json format, we surrounded the data set with brackets and separated each day's query with a comma (we formed separate .txt files for each keyword). We created arrays for each of the elements in the .txt file, then transposed that array so that each index returned the elements we extracted from the tweet. Next, these arrays were written to a .csv file, then concatenated together to form a file over 22,000 lines long.
Unfortunately, only 1-5% of tweets contain latitude and longitude coordinates. Since the main objective of this project is to plot these very coordinates on a map, finding a solution to this issue was a very high priority. We took all of the unique locations of users profiles from the tweets we collected, eliminated the invalid ones, like "~positive vibes~", and used the geopy python package to find the lat and long values at each of these locations. Because there were almost 1400 unique valid locations, we split the array into 14 smaller arrays to more effectively find overlooked invalid data and not kill my computer (all 14 sets of code took about 30 minutes collectively to run). Our code wrote the location and its lat and long coordinates to a new .csv file. We used a VLOOKUP function in excel to find occurances of each valid location in the full data set. Using this code, we were able to assign lat and long coordinates to over 5,000 of our tweets!
The main visualization for this project is rendered using the dc leaflet library and more specifically, the first example on this page. It was actually surprisingly easy to edit the index.html file to function using my data set once I changed the variable names. However, the pie chart in the original visualization definitely had to go, so I decided to use a horizontal bar chart instead. Since we had so many keywords, we trimmed the data to only include the top 19 search terms so that the names of the keywords would be legible on the bar chart.
Hollywood Feed is a growing brick and mortar pet food retailer that relies on extensive employee education and expert product selection to provide unmatched customer service and has more than 40 stores in the southeast. Internet and social media presence haven't been much of a priority until now. Hollywood Feed is now opening stores hundreds of miles away from established, well-known stores and could definitely use twitter data to their advantage.
We want to use data visualizations to answer these questions:
Who are Hollywood Feed's customers?
What do they tweet about?
Where are other people like them?
Where are our competitors' customers?
Where are our competitors more popular than we are?
This is probably the broadest set of questions we could have come up with, but since we didn't have much data to go off of, this seemed like a great way to get preliminary information about customers and competitors. Since Hollywood Feed is a regional retailer, location is really important. We are much more concerned about the Petco across the street than the locations on the west coast. A map seemed like the most logical solution. In order to determine which keywords are more important than others, this map had to be interactive with some sort of keyword selection.
I also think it would be interesting to compare our competitors to one another in terms of their customer engagement (the number of times users mention them in tweets) as well as their popularity (how popular their tweets are). We summed the number of lines in our "@" searches for each competitor on each day to get the total number of mentions and summed the total number of retweets and favorites per competitor per day to get the popularity. We used grouped bar charts to represent both of these data sets. The only real issue we faced in accurately displaying customer engagement is that on April 12, Peta started a trail of negative retweets about Petco and Petsmart in response to National Pet Day. This heavily skewed the data on that day, and the sentiment analysis could not detect enough negativity to counteract this issue. The graph still accurately represents the number of mentions, but it does not account for sentiment.
This data set can be a little confusing for those of you who aren't familiar with Hollywood Feed competitors and products.
Here's a description of each of the keyword terms you can find on the map visualization on the home page:
At Chewy: tweets mentioning Chewy's twitter handle, @chewy
Dog Park: tweets containing the words "dog" and "park"
Petsmart: tweets containing the word "Petsmart"
Pet Food: tweets containing the words "pet" and "food"
At Petsmart: tweets mentioning Petsmart's twitter handle, @petsmart
Petco: tweets containing the word "Petco"
Dog Treats: tweets containing the words "dog" and "treats"
Rescue Dog: tweets containing the words "rescue" and "dog"
At Petco: tweets mentioning Petco's twitter handle, @petco
At Pet Supplies Plus: tweets mentioning Pet Supplies Plus's twitter handle, @petsuppliesplus
Hollywood Feed: tweets containing the phrase "Hollywood Feed"
Blue Buffalo: tweets containing the phrase "Blue Buffalo"
Dog Food: tweets containing the words "dog" and "food"
Healthy Pet Food: tweets containing the words "healthy", "pet", and "food"
Puppy Image: tweets containing the word "puppy" and an image
Dog Image: tweets containing the word "dog" and an image
Natural Dog Food: tweets containing the word "natural", "dog", and "food"
Natural Balance: tweets containing the phrase "natural balance"
From Pet Supplies Plus: tweets from Pet Supplies Plus's account, @petsuppliesplus
Petco, Petsmart, Chewy, and Pet Supplies Plus are all retailers we are interested in tracking. Blue Buffalo and Natural Balance are both brands of dog food carried in Hollywood Feed stores.
How do I use this thing?
If you would like to select one or more keywords at a time, simply click on each keyword you would like to see, and the map will change accordingly.
If you would like to select one or more marker (the blue teardrops on the map) at a time, simply click on each point you would like to see, and the map will change accordingly. Note: the map will reset if you have selected a marker and drag it out of the window. Also, the number of markers is not the number of tweets but the number of locations where tweets can be found. You can view the total number of tweets at a location by hovering over the marker.
Most importantly, we would have started running queries as soon as possible after the semester started. Having two weeks of data is probably adequate for this project, but an entire semester's worth would have been much better.
If we had had more data, we could have seen which keywords were not returning the data we had originally intended it to return. Pet food brand searches such as Fromm and Acana mainly produced tweets in other languages. Setting an additional query parameter to "&lang=en" might have mitigated this issue. We also could have performed queries which pulled tweets from the brand's twitter account, like Acana's account, @ACANApetfood.
For the map visualization, we would have liked to encode the total number of tweets in each bin (the circle that shows up when you zoom out). It can be confusing to viewers when there are multiple tweets at one location. It would have also been helpful to somehow encode the text of the tweet into the attributes of the marker. I think it would be really awesome to take a sample of up to random ten tweets from that location and display the sentiment of each tweet. For the horizontal bar graph below the map, I would have prefered that the x axis changed according to the highest selected value so that when you have selected a data point containing 43 tweets, you wouldn't have to hover over each keyword to see how many times it was found in that data set.
For the competitor mentions and popularity graphs, we simply ran out of time. Our main objective was to do the map visualization well, and we did not have much time left over to work on the remaining two visualizations. Clearly, both graphs should instead be line graphs since they are plotting trends over time. We also tried to insert a tooltip into both graphs but could not get it to work. If we had more time, we would also have a filter at the top of each graph to select each of the different competitors.
In retrospect, I think we spent about 90% of our time on this project just formatting the data correctly for d3. Had we known that the data formatting would take such a long time, we would have started coding much earlier and would have had time to properly develop our other visualizations. This project has definitely sparked a personal interest in data visualizations, and I look forward to developing more in the future.


Available FMUs¶
CYME FMU¶
The CYME FMU can be downloaded from here.
- An example of model description is provided below. A few things are required for the CYME FMU to operate properly:
- a parameter called _configurationFileName pointing to a JSON configuration file (including the feeder filename).
- inputs and outputs should have the following format {network ID}!{node/load ID}!{keyword}, where keyword can be KW, KVAR, or Vpu.
<?xml version="1.0" encoding="UTF-8"?>
<SimulatorModelDescription
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
fmiVersion="2.0"
modelName="simulator"
description="Input data for a Simulator FMU"
generationTool="SimulatorToFMU">
<ModelVariables>
<ScalarVariable
name="_configurationFileName"
description="parameter"
causality="parameter"
start="C:\\Users\\jonathan.json2"
type="String">
</ScalarVariable>
<ScalarVariable
name="IEEE34NODES!836!KW"
description="input P"
causality="input"
type="Real"
unit="kW">
</ScalarVariable>
<ScalarVariable
name="IEEE34NODES!836!KVAR"
description="input Q"
causality="input"
type="Real"
unit="kW">
</ScalarVariable>
<ScalarVariable
name="voltage!836!Vpu"
description="output voltage p.u."
causality="output"
type="Real">
</ScalarVariable>
</ModelVariables>
</SimulatorModelDescription>
The JSON configuration file should have the following format:
{
"model_filename": "IEEE_34_node_test_feeder.sxst",
"total_load_filename":"total_load.csv",
"substation_network":"IEEE34NODES"
}
PandaPower FMU¶
The PandaPower FMU can be downloaded here.
An example of model description is provided below. Note that this FMU does not require any configuration file since the feeder model is set within the FMU.
The format for inputs and outputs is defined as {keyword}_{node id} where keyword can be KW, KVAR, and Vpu.
<?xml version='1.0' encoding='UTF-8'?>
<SimulatorModelDescription
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
fmiVersion="2.0"
modelName="pandapower"
description="pandapower" generationTool="SimulatorToFMU">
<ModelVariables>
<ScalarVariable name="KW_7"
description="_" causality="input" type="Real" unit="_"/>
<ScalarVariable name="KVAR_7"
description="_" causality="input" type="Real" unit="_"/>
<ScalarVariable name="Vpu_7"
description="_" causality="output" type="Real" unit="_"/>
<ScalarVariable name="Vpu_12"
description="_" causality="output" type="Real" unit="_"/>
</ModelVariables>
</SimulatorModelDescription>
Python FMU Template¶
A simple template for creating Python FMU can be found here.
Note: If the name of the FMU is set to “NAME” in the XML file then the Python file with the “exchange” function should be named “NAME_wrapper.py”
Note: The FMU needs to be recompiled anytime it is moved to a different folder or if any inputs/outputs names changed.


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The last experience I had with psychedelic mushrooms was probably my sixth. It was almost (3) years ago.
I was in Amsterdam with my girlfriend at the time. She remains the girl I have had the longest relationship with in my life. We had a common past of somewhat heavy marijuana use. She had taken mushrooms only once before and I had already used them maybe (5) times. We each got an eight of Mexican mushrooms in the Smart Shop. We asked extensive questions concerning the effects of the variety of mushrooms the shop offered, and the guy who ran the shop and sold us the sacred shrooms, answered our questions based on his own experiences with the different types. I think this gave my girlfriend a confidence that I resisted because I already had a knowledge of mushrooms.
The visual peak of my trip happened soon after we ate the mushrooms on empty stomachs and followed their ingestion with famous Dutch frites. We walked through a square in the city on our way to Vondel Park and came across a gathering of pigeons. They all flapped their wings and took off from the ground at the same time and their energy made me see them as spectacular, 3-dimensional glints of light. The geometry of their movement overtook me and I greatly enjoyed this unexpected insight into a familiar scene.
On our way to Vondel Park, I had an exchange of glance with a man who struck me as particularly scary. He seemed to be a native of the city of Amsterdam, and he freaked me out. I was happy to get to the park. In the park, my girlfriend remarked at how squishy the wet ground felt. When I paid attention to this thought, I felt what she meant, but I didn't find it particularly interesting. I was looking for something interesting. We drank some orange juice and my legs felt carbonated, quite a pleasant feeling.
Then we came upon graffiti on the bridges and stone walls. My girlfriend looked at the art and it spoke to her. She marveled at the movement she saw and the underlying implications. I looked at the same art and felt the ability to make it move; I felt that I could let myself get lost in the graffiti and see it surprise me, but I didn't want to because I felt that that was a waste of this power I had. I felt that I had an especially visceral sensitivity to my surroundings, but I didn't want to waste this sensitivity on some paint and a brick wall. I wanted to see and feel the energy of nature: mountains and trees.
At one point we sat down near some water. I was climbing on a tree trunk that bended oddly, the water. Then I went and sat with my girlfriend on a bench.  They were three girls probably between the ages of 5 and eight. The five year-old road on the back of the 8 year-old's bicycle. I watched them scamper on the trunk while we smoked our joint. I felt a little guilty smoking a joint when children were so near, but we were in a park in Amsterdam so we finished the joint without any legal worries. We sat and chilled and the girls finally mounted their bikes to rides somewhere else.
 The youngest carried a vibrant flower. They looked like one chain of angels to me, riding away single file like they did. As they rode away, the youngest girl on the back of one of the bikes looked back in my direction and I saw a large eye in the middle of her forehead, her third eye, the energy eye. She was a divine entity; a pure, unadulterated messenger of life, a vessel for energy. And she carried a flower. I felt touched by divinity. I felt confirmation of a force of nature I had only vaguely defined before. I felt blessed by her.
Then we continued our journey and the graffiti seemed a waste of time, and my girlfriend's marveling of the 'squishy' ground was lost to me.
I felt I couldn't relate to my girlfriend. I felt the underlying alienation that existed in our relationship and this troubled me.
I learned from this last experience the following: The power of sensitivity that mushrooms activate is a holy power that should only be applied to nature. Tripping out to man-made shapes and colors is an abuse of that power. If I ever do mushrooms again, it must be an occasion in unadulterated nature. Only then will the mushrooms align me with my surroundings and interest me with their unique insight.
During the trip, I tripped out about the relationship between my girlfriend and me. I need to be with someone I can be completely honest with. Ideally, I'd like to be completely honest with my girlfriend, but I definately have issues with that... The other day my girlfriend asked me if I wanted to do mushrooms with her and I said no because I'm too unsure about our relationship.
Psyilocbian mushrooms are a powerful tool, but they must be used consciously and sparingly.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Idea
JID, the conjointed project of schools from the Czech Republic and Poland, was formed by
a firm named Erasmus. Our school was chosen from many other contestants. We are
a class of 14 people and our work on the project is the IT part, as you can see on
this site that we made. The Polish students have the logistics skills that are needed for this
project aswell.
Our goal
The main objective is an exchange of methodology and good practices in the field of logistics
and information technology. The project aims to raise awareness of ways and specific
requirements of road and water transport and to deepen the knowledge of the partners’ cultures
at the same time. The choice of the project goal is related to the regional specifics.
A subsequent objective is to embed gained experience, outcomes and examples of good
practice into the education process. Knowledge and skills will be passed on in the form of
peer-to-peer learning among students and teachers, as well.


Travel Blog
Spain
Middle East
Italy
South of France
Why Travel?
To be spontaneous and emrse onself in different cultures.
Madrid
Florence
Israel
Saint Jean Cap Ferrat


A class that gives records a location in the query. More...
#include <LocationInfo.hpp>
Public Member Functions | |
| LocationInfo () | |
| Constructor that sets the information to 0. | |
| LocationInfo (const XMLCh *file, unsigned int line, unsigned int column) | |
| Constructor that takes the initial information. | |
| const XMLCh * | getFile () const |
| The file that the query was parsed from. | |
| unsigned int | getLine () const |
| The line in the query. | |
| unsigned int | getColumn () const |
| The column in the query. | |
| void | setLocationInfo (const XMLCh *file, unsigned int line, unsigned int column) |
| Sets the location to the given information. | |
| void | setLocationInfo (const LocationInfo *o) |
| Sets the location with the information in the given LocationInfo object. |
A class that gives records a location in the query.
| LocationInfo::LocationInfo | ( | ) | [inline] |
Constructor that sets the information to 0.
| LocationInfo::LocationInfo | ( | const XMLCh * | file, | |
| unsigned int | line, | |||
| unsigned int | column | |||
| ) | [inline] |
Constructor that takes the initial information.
| unsigned int LocationInfo::getColumn | ( | ) | const [inline] |
The column in the query.
| const XMLCh* LocationInfo::getFile | ( | ) | const [inline] |
The file that the query was parsed from.
This could be null.
| unsigned int LocationInfo::getLine | ( | ) | const [inline] |
The line in the query.
| void LocationInfo::setLocationInfo | ( | const LocationInfo * | o | ) | [inline] |
Sets the location with the information in the given LocationInfo object.
| void LocationInfo::setLocationInfo | ( | const XMLCh * | file, | |
| unsigned int | line, | |||
| unsigned int | column | |||
| ) | [inline] |
Sets the location to the given information.


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new Response(envelope, match)
Represents an wrapped response object
Parameters:
| Name | Type | Description |
|---|---|---|
envelope |
Object | Envelope dynamic object containing message information |
match |
Array.<String> | Array of matches from the message regex |
Properties:
| Name | Type | Description |
|---|---|---|
match |
Array.<String> | Array of matches from the message regex |
envelope |
Envelope | This response {Envelope} instance |
user |
User | The {User} who sent the message that generated this Response instance |
channel |
Channel | {Channel} from which this message came |
text |
String | This message's content |
- Source:
Methods
addReaction(reactionName) → {Promise}
Adds an reaction to this message. This feature depends on the current adapter implementation and will be rejected by default.
Parameters:
| Name | Type | Description |
|---|---|---|
reactionName |
String | Emoji name to be added to the message. |
- Source:
Returns:
A Promise that will be resolved after the reaction has been added to the target message. Again, notice that this feature depends on the current Adapter being used and will be rejected by default.
- Type
- Promise
allowPropagation() → {undefined}
Sets the propagation status to allowed, which allows the next handler to try to process the incoming message
- Source:
Returns:
Nothing
- Type
- undefined
ask(message, type) → {Promise}
Replies this message with a question.
Parameters:
| Name | Type | Description |
|---|---|---|
message |
String | Prompt to be sent to the user. |
type |
Number | Type of expected answer. Possible values are Context.NUMBER,
Context.BOOL, and Context.REGEX. Documentation about those items and
how they behave can be found in the |
- Source:
Returns:
A Promise that will be resolved whenever the target user replies to the prompt in the given Channel. This promise cannot be rejected, but it may never be resolved.
- Type
- Promise
getUser() → {Promise}
Gets an updated state of the user who sent this message.
- Source:
Returns:
A Promise that will be resolved whenever the user is found.
- Type
- Promise
reply(string) → {undefined}
Replies the incoming message mentioning the user if necessary
Parameters:
| Name | Type | Description |
|---|---|---|
string |
String | String to be sent back to the user |
- Source:
Returns:
Nothing
- Type
- undefined
send(string) → {undefined}
Replies the incoming message
Parameters:
| Name | Type | Description |
|---|---|---|
string |
String | String to be sent back to the user |
- Source:
Returns:
Nothing
- Type
- undefined
sendTyping() → {undefined}
Sends a feedback to the channel or user that sent the incoming message to the bot. This feature depends on the implementation of the Adapter in use, and may not have effect. For instance, when using the Slack adapter, the result is an typing indicator on the bottom of the window, stating the the user behind the current bot is typing. This state is reseted automatically, whenever a new message from this bot appears.
- Source:
Returns:
Nothing.
- Type
- undefined


GET /taas/now
{ "hours": 17, "minutes": 31, "seconds": 35 }
GET /taas/now?timezone=Europe/London
{ "hours": 1, "minutes": 31, "seconds": 35 }
Get the current hours, minutes, and seconds in JSON.
By default, this will be in the timezone of the server, but you can optionally pass a ?timezone=:timezone
query parameter to use a different timezone.


class Solver
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
private int |
log10 |
private int[][] |
matrix |
private java.lang.String |
numberFormat |
private int |
size |
| Constructor and Description |
|---|
Solver(int[][] matrix)
This constructor sets the matrix to be used, and calls the solving method.
|
| Modifier and Type | Method and Description |
|---|---|
private boolean |
contains(int row,
int col,
int val)
This is a brother method to isLegal, it checks if a value is already used in the row or column.
|
private boolean |
containsInBox(int row,
int col,
int val)
This is a brother method to isLegal, it checks if a value is already used in the 3x3 box.
|
private boolean |
isLegal(int row,
int col,
int val)
This method checks if all values are legal, based on the basic Sudoku rules.
|
private boolean |
solve(int row,
int col)
This method contains the main backtracking code used to find the solution for the Sudoku stored in a 2d array.
|
java.lang.String |
toString()
This toString method is adapted from a StackOverflow response.
|
private final int size
private final int[][] matrix
private final int log10
private final java.lang.String numberFormat
public Solver(int[][] matrix)
matrix
- This is the previously used matrix as defined in the main class.private boolean solve(int row, int col)
row
- The base value is 0, but the method changes it through each iterationcol
- Same as column, used for each searchprivate boolean isLegal(int row, int col, int val)
row
- The row to be checkedcol
- The column to be checkedval
- The input value to be checkedprivate boolean contains(int row, int col, int val)
row
- The row to be checkedcol
- The column to be checkedval
- The value to be checked in row and columnprivate boolean containsInBox(int row, int col, int val)
row
- The row in which the input is insidecol
- The column in which the input value is insideval
- The value to be checkedpublic java.lang.String toString()
toString
in class java.lang.Object


Included:
* BF4 weapon filter category updates - now uses internally battlelog categories to be consistent with battlelog
* emblem editor fixed after you've imported a new emblem - currently it have broken the editor (especially the delete buttons)
Topicstarter
BrainFooLong
Administrator
Well, realised that the emblem import fix does not really work.
Still buggy. Working on it.


Top Git
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Finding the best github Projects
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When we work with us,we are part of a team of committed collaborators.All aspects of our software development process are intergrated,interactive
and agile
Our approach unifies,development and product managment to create exeptional products.
We start each project by understanding your business goals,the user's needs and product requirements
Then we help translate them into effective and elegants solutions.
SERVICES
Before we sign a contract or write a line of code our team will spend the necessary time needed to understand your vision
The outcome of this scoping session is a proposal or high-level statement of work.
based on your requirements,we can provide a complete offering or just the pieces you need.
Our design practice offers a full range of services
including brand strategy, interaction and visual design
and user experience testing.
Throughout your project, our designs anad workflows, solocit user
feedback and work with you to make sure what gets built
is what is needed.
All engineers are fluent in the latest enterprise, mobile
and web development technologies.
They collaborate with your team to write, and improve
code on daily basis, using proven practices such
test-driven development and pair programming.
Planning and development is interactive. Because we are
constantly coding and testing , the products we build are
are ready to go live.
This iterative process allows for changes as business
requirments evolve.
DELANI STUDIO
2019


EPL: Tottenham Hotspur Sack Manager Pochettino
by George OgboluStruggling English Premier League (EPL) side Tottenham Hotspur have announced the sacking of manager Mauricio Pochettino.
Spurs are currently languishing in 14th place in the Premier League following a poor start to the season which has left them 20th points behind leaders Liverpool.
Pochettino’s coaching staff Jesus Perez, Miguel D’Agostino and Antoni Jimenez have been relieved of their duties.
Speaking on the sack, Tottenham chairman Daniel Levy said: “We were extremely reluctant to make this change and it is not a decision the Board has taken lightly, nor in haste.
“Regrettably domestic results at the end of last season and beginning of this season have been extremely disappointing.
“It falls to the Board to make the difficult decisions – this one made more so given the many memorable moments we have had with Mauricio and his coaching staff – but we do so in the Club’s best interests.
“Mauricio and his coaching staff will always be part of our history. I have the utmost admiration for the manner in which he dealt with the difficult times away from a home ground whilst we built the new stadium and for the warmth and positivity he brought to us. I should like to thank him and his coaching staff for all they have contributed. They will always be welcome here.
“We have a talented squad. We need to re-energise and look to deliver a positive season for our supporters.”


Document Manipulator
This component exists to test if various "document" functions have been overridden to point from the Spec IFrame to the App IFrame.
- documentQuerySelector
- documentBodyQuerySelector
- documentQueryAllSelector
- documentBodyQueryAllSelector


| Order No. |
Create Time |
Customer Name |
Origin |
Destination |
Destination Station |
Cargo Name |
Trade Type |
BOL No. |
Container Quantity |
Container Quantity |
Receivable CNY |
Receivable USD |
Receivable SUM |
Received in advance time |
Receivable Balance |
Invoice Balance CNY(CNY) |
Invoice Balance USD(USD) |
|
|
|
|
Origin |
Destination |
Rail Destination |
|
Trade Type |
|
Container Quantity |
|
Receivable CNY |
Receivable USD |
Receivable SUM |
|
Receivable Balance |
Invoice Balance CNY |
Invoice Balance USD |
| Sum |
|
|
|
|
|
|
|
|
Container Quantity |
|
Receivable CNY |
Receivable USD |
Receivable SUM |
|
Receivable Balance |
Invoice Balance CNY |
Invoice Balance USD |


Originally posted on Oct 18, 2014 in my previous blog.
In this problem we need to calculate the number of inversions. a
contains numbers from 1 to n in a specific order.
An inversion here is defined as a(i) > a(j) && i<=j
in one permutation. So the problem reduces to finding those sequences over all
permutations such that they have exactly k
permutations. This can be done by brute force - listing out all permutations of numbers (from 1 to n)
and counting those sequences which have exactly k inversions. Since listing all the permutations takes O(n!)
, this method would be computationally
intensive for large values of n.
We can try a recursive approach for this problem. Note that if n = 0
, number of permutations is 0, and if k = 0
number of permutations is 1. The recursion is shown below,
permut(n) = {
0 if n == 0;
1 if k == 0;
sum(permut(n - 1, k - i)) (for 0 <= i < n) otherwise
}
This recursion can be coupled with memoization to take care of redundant solving of subproblems. (NOTE: if there are many queries,
it is better to follow a bottom-up approach rather than memoization. In this particular problem there are less than 10 queries). Initialize a 2d array
dp[n][k]
with -1.
permut(int n, int k) {
// boundary cases
if(n == 0)
return 0;
if(k == 0)
return 1;
if(dp[n][k] != -1)
return dp[n][k];
int sum = 0;
for(int i = 0; i < n; i++) {
sum += permut(n - 1, k - i);
}
return (dp[n][k] = sum);
}
Happy Memoization!


Before Change
])
def test_4fold(self):
self._write_tfrecord("train", 5, "abcdefghijkl")
instructions = [
tfrecords_reader.ReadInstruction("train", from_=k, to=k+25, unit="%")
for k in range(0, 100, 25)]
tests = self.reader.read("mnist", instructions, self.SPLIT_INFOS)
instructions = [
(tfrecords_reader.ReadInstruction("train", to=k, unit="%") +
tfrecords_reader.ReadInstruction("train", from_=k+25, unit="%"))
for k in range(0, 100, 25)]
trains = self.reader.read("mnist", instructions, self.SPLIT_INFOS)
read_tests = [list(r) for r in tfds.as_numpy(tests)]
read_trains = [list(r) for r in tfds.as_numpy(trains)]
self.assertEqual(read_tests, [[b"a", b"b", b"c"],
After Change
])
def test_4fold(self):
train_info = self._write_tfrecord("train", 5, "abcdefghijkl")
instructions = [
tfrecords_reader.ReadInstruction("train", from_=k, to=k+25, unit="%")
for k in range(0, 100, 25)]
tests = self.reader.read("mnist", instructions, [train_info])
instructions = [
(tfrecords_reader.ReadInstruction("train", to=k, unit="%") +
tfrecords_reader.ReadInstruction("train", from_=k+25, unit="%"))


A collection of components used to build engines
Infinite reservoir
Information
This infinite reservoir model is capable of supplying any amount of mass or energy (via the mdot and q flow variables on the
Gas connector) to sustain a specified temperature and pressure.
These "infinite" models are interesting because instead of including constitutive relationships between
flow values (mass flow and power) and the potentials of the system (pressure and temperature), they instead
provide algebraic constraints involving only the states. It is assumed that the flows will be calculated
such that the constraints will be satisfied. This kind of model is analagous to an ideal voltage source (supplies
as much current as is necessary to sustain a specified voltage) or a mechanical ground (supplies as much
force as is necessary to sustain a specified position).
Parameters
| Name | Default | Description |
| P | 101800 | Reservoir pressure [Pa] |
| T | 300 | Reservoir temperature [K] |
Modelica definition
model Reservoir "Infinite reservoir"
parameter Modelica.SIunits.Pressure P=101800 "Reservoir pressure";
parameter Modelica.SIunits.Temperature T=300
"Reservoir temperature";
Interfaces.Gas tap;
equation
tap.P = P;
tap.T = T;
end Reservoir;
Collection of parts for a complete individual cylinder
Information
All the basic componets of a single cylinder, 2-valve engine
have been compiled in this model.
Parameters
| Name | Default | Description |
| spark_advance | | Spark advance [deg] |
| burn_duration | | Burn Duration [deg] |
| evo | 40 | Exhaust Valve Opening [deg] |
| ivo | 150 | Intake Valve Opening [deg] |
| evc | 205 | Exhaust Valve Closing [deg] |
| ivc | 310 | Intake Valve Closing [deg] |
| crank_shift | 0 | Crankshaft Shift [deg] |
| ivd | 0.032 | Intake Valve Diameter [m] |
| evd | 0.028 | Exhaust Valve Diameter [m] |
Modelica definition
model IndividualCylinder
"Collection of parts for a complete individual cylinder"
extends Interfaces.Cylinder;
parameter Types.Degrees spark_advance "Spark advance";
parameter Types.Degrees burn_duration "Burn Duration";
parameter Types.Degrees evo=40 "Exhaust Valve Opening";
parameter Types.Degrees ivo=150 "Intake Valve Opening";
parameter Types.Degrees evc=205 "Exhaust Valve Closing";
parameter Types.Degrees ivc=310 "Intake Valve Closing";
parameter Types.Degrees crank_shift=0 "Crankshaft Shift";
parameter Modelica.SIunits.Diameter ivd=0.032
"Intake Valve Diameter";
parameter Modelica.SIunits.Diameter evd=0.028
"Exhaust Valve Diameter";
Engine.Components.MasslessPiston piston;
Engine.Components.CrankSlider crankslider;
Engine.Components.ControlVolume combustion_chamber;
Engine.Components.Valve intake_valve(dia=ivd);
Engine.Components.Valve exhaust_valve(dia=evd);
Engine.Components.TimingBelt timing_belt;
Engine.Components.Cam intake_cam(vo=ivo, vc=ivc);
Engine.Components.Cam exhaust_cam(vo=evo, vc=evc);
Engine.Components.Combustion combustion_model(burn_duration=burn_duration
);
Engine.Components.SparkControl spark_control(spark_advance=spark_advance)
;
Engine.Components.ChamberVolume chamber_volume;
OffsetShaft offset_shaft(shift=crank_shift);
equation
connect(crankslider.geom, geom);
connect(chamber_volume.geom, geom);
connect(intake_valve.a, intake);
connect(exhaust_valve.a, exhaust);
connect(piston.chamber, combustion_chamber.state);
connect(intake_cam.valve_lift, intake_valve.lift);
connect(exhaust_cam.valve_lift, exhaust_valve.lift);
connect(intake_cam.camshaft, timing_belt.camshaft);
connect(exhaust_cam.camshaft, timing_belt.camshaft);
connect(intake_valve.b, combustion_chamber.state);
connect(combustion_chamber.state, exhaust_valve.b);
connect(combustion_model.cylinder, combustion_chamber.state);
connect(combustion_model.crank, spark_control.crank);
connect(spark_control.spark, combustion_model.start);
connect(chamber_volume.volume, combustion_chamber.volume);
connect(combustion_chamber.mass, combustion_model.mass);
connect(piston.geom, geom);
connect(piston.piston, crankslider.piston);
connect(chamber_volume.piston, crankslider.piston);
connect(offset_shaft.cyl, crankslider.crank);
connect(offset_shaft.crank, crankshaft);
connect(timing_belt.crankshaft, crankslider.crank);
connect(spark_control.crank, crankslider.crank);
end IndividualCylinder;
Ideal Dynamometer
Information
This dynamometer model is ideal. This means that the dynamometer appear (to anything connected to it) to have
an infinite mass. It is important that the input signal is continuous. Furthermore the input signal has units
of "revolutions per minute".
Parameters
| Name | Default | Description |
| cycle_fraction | 1.0 | Average over what fraction of the cycle |
Modelica definition
model Dynamometer "Ideal Dynamometer"
parameter Real cycle_fraction=1.0
"Average over what fraction of the cycle";
Modelica.SIunits.Energy work;
Modelica.SIunits.Energy last_work;
Modelica.SIunits.Torque avg_tau;
Modelica.SIunits.Angle next_rotation;
Types.RPM avg_rpm;
Modelica.SIunits.Time previous_time;
Modelica.Blocks.Interfaces.InPort rpm(final n=1);
Modelica.Mechanics.Rotational.Interfaces.Flange_a shaft;
equation
der(shaft.phi) = rpm.signal[1]*Modelica.Constants.pi/30;
der(work) = shaft.tau*der(shaft.phi);
algorithm
when initial() then
next_rotation := shaft.phi + cycle_fraction*4*Modelica.Constants.pi;
last_work := 0;
previous_time := time;
avg_rpm := rpm.signal[1];
end when;
when shaft.phi > next_rotation then
next_rotation := next_rotation + cycle_fraction*4*Modelica.Constants.pi;
avg_tau := (work - pre(last_work))/(cycle_fraction*4*Modelica.Constants.pi
);
avg_rpm := 120*cycle_fraction/(time - pre(previous_time));
last_work := work;
previous_time := time;
end when;
end Dynamometer;
An Inline 4 Cylinder Engine
Information
An assembly of components required for an I4 engine. This model requires a connection to intake and exhaust
gases, the crankshaft and geometry information.
Parameters
| Name | Default | Description |
| spark_advance | | Spark advance [deg] |
| burn_duration | | Burn Duration [deg] |
| evo | 40 | Exhaust Valve Opening [deg] |
| ivo | 150 | Intake Valve Opening [deg] |
| evc | 205 | Exhaust Valve Closing [deg] |
| ivc | 310 | Intake Valve Closing [deg] |
| ivd | 0.032 | Intake Valve Diameter [m] |
| evd | 0.028 | Exhaust Valve Diameter [m] |
Modelica definition
model I4_Engine "An Inline 4 Cylinder Engine"
extends Interfaces.Engine;
parameter Types.Degrees spark_advance "Spark advance";
parameter Types.Degrees burn_duration "Burn Duration";
parameter Types.Degrees evo=40 "Exhaust Valve Opening";
parameter Types.Degrees ivo=150 "Intake Valve Opening";
parameter Types.Degrees evc=205 "Exhaust Valve Closing";
parameter Types.Degrees ivc=310 "Intake Valve Closing";
parameter Modelica.SIunits.Diameter ivd=0.032
"Intake Valve Diameter";
parameter Modelica.SIunits.Diameter evd=0.028
"Exhaust Valve Diameter";
Modelica.SIunits.Power power "Instantaneous engine power output";
replaceable model CylinderType = IndividualCylinder extends
Interfaces.Cylinder;
CylinderType cylinder1(
crank_shift=0,
spark_advance=spark_advance,
burn_duration=burn_duration,
evo=evo,
ivo=ivo,
evc=evc,
ivc=ivc,
ivd=ivd,
evd=evd);
CylinderType cylinder2(
crank_shift=360,
spark_advance=spark_advance,
burn_duration=burn_duration,
evo=evo,
ivo=ivo,
evc=evc,
ivc=ivc,
ivd=ivd,
evd=evd);
CylinderType cylinder3(
crank_shift=540,
spark_advance=spark_advance,
burn_duration=burn_duration,
evo=evo,
ivo=ivo,
evc=evc,
ivc=ivc,
ivd=ivd,
evd=evd);
CylinderType cylinder4(
crank_shift=180,
spark_advance=spark_advance,
burn_duration=burn_duration,
evo=evo,
ivo=ivo,
evc=evc,
ivc=ivc,
ivd=ivd,
evd=evd);
Modelica.Mechanics.Rotational.Inertia crankshaft_inertia(J=0.03, w(start=
157));
equation
connect(cylinder1.intake, intake);
connect(cylinder3.intake, intake);
connect(cylinder2.intake, intake);
connect(cylinder4.intake, intake);
connect(cylinder1.exhaust, exhaust);
connect(cylinder4.exhaust, exhaust);
connect(cylinder3.exhaust, exhaust);
connect(cylinder2.exhaust, exhaust);
connect(cylinder4.geom, engine_geometry);
connect(cylinder2.geom, engine_geometry);
connect(cylinder3.geom, engine_geometry);
connect(cylinder1.geom, engine_geometry);
connect(cylinder1.crankshaft, cylinder3.crankshaft);
connect(cylinder2.crankshaft, cylinder3.crankshaft);
connect(cylinder4.crankshaft, cylinder2.crankshaft);
connect(crankshaft_inertia.flange_b, cylinder1.crankshaft);
connect(crankshaft_inertia.flange_a, crankshaft);
power = crankshaft.tau*der(crankshaft.phi);
end I4_Engine;
A simple filling-and-emptying manifold model
Information
This very simple model contains a throttle and a control volume which
can be connected to the intake system of an engine (or cylinder) in
order to simulate the effects of manifold filling and emptying. A
throttle position of 90 degrees corresponds to wide open throttle (WOT)
while a throttle position of 0 degrees corresponds to a closed throttle.
Parameters
| Name | Default | Description |
| volume | 0.004 | Manifold volume [m3] |
Modelica definition
model Manifold "A simple filling-and-emptying manifold model"
parameter Modelica.SIunits.Volume volume=0.004 "Manifold volume";
Engine.Components.ControlVolume manifold_volume;
Modelica.Blocks.Sources.Constant volume_value(final k={volume});
Interfaces.Gas ambient;
Interfaces.Gas manifold;
Modelica.Blocks.Interfaces.InPort throttle_angle(final n=1);
Engine.Components.Throttle throttle(dia=0.10);
equation
connect(volume_value.outPort, manifold_volume.volume);
connect(manifold_volume.state, manifold);
connect(throttle.b, manifold_volume.state);
connect(throttle.a, ambient);
connect(throttle_angle, throttle.throttle_angle);
end Manifold;
Thermodynamic control volume
Information
This model contains the basic equations of a thermodynamic control volume. These include the conservation of mass and energy
as well as the ideal gas law and gas property equations. The volume of the control volume is an input to this model
and the current mass contained within the control volume is an output. The output signal for mass is necessary to
connect a combustion model.
Modelica definition
model ControlVolume "Thermodynamic control volume"
protected
Modelica.SIunits.Energy U(start=147) "Total energy";
Modelica.SIunits.Mass m(start=4e-4) "Total mass";
Modelica.SIunits.Pressure P(start=101800) = state.P "Pressure";
Modelica.SIunits.Temperature T(start=300) = state.T "Temperature";
Modelica.SIunits.MassFlowRate mdot=state.mdot "Net mass flow";
Modelica.SIunits.Power q=state.q "Net power";
Modelica.SIunits.Volume V=volume.signal[1] "Chamber volume";
Modelica.SIunits.AmountOfSubstance N "Number of moles of gas";
Real R=Modelica.Constants.R;
Real logV;
Real logP;
public
Modelica.Blocks.Interfaces.InPort volume(final n=1);
public
Modelica.Blocks.Interfaces.OutPort mass;
public
Interfaces.Gas state "Gas state";
protected
PropertyModel props(T=T, P=P);
equation
// Compute number of moles
N = m/props.mw;
// Ideal gas low
P*V = N*R*T;
// First law of thermodynamics
der(U) = q - P*der(V);
U = m*props.u;
// Conservation of mass
der(m) = mdot;
mass.signal[1] = m;
logV = Modelica.Math.log(V);
logP = Modelica.Math.log(P);
end ControlVolume;
Engine poppet valve
Information
This is a very simple model of an engine valve the is derived from the "Orifice" model of isentropic flow.
The valve model must be connected to two different gas volumes (or reservoirs). In addition, a translational
connector is used to represent the lift of the valve.
Parameters
| Name | Default | Description |
| dia | 0.012 | Valve diameter [m] |
| max_lift | 0.012 | Maximum Valve Lift [m] |
| max_discharge | 0.7 | Maximum Discharge Coefficient |
Modelica definition
model Valve "Engine poppet valve"
parameter Modelica.SIunits.Diameter dia=0.012 "Valve diameter";
parameter Modelica.SIunits.Length max_lift=0.012
"Maximum Valve Lift";
parameter Real max_discharge=0.7 "Maximum Discharge Coefficient";
extends Orifice(final Aref=Modelica.Constants.PI*(dia/2)^2);
protected
parameter Real c_over_l=max_discharge/max_lift;
public
Modelica.Mechanics.Translational.Interfaces.Flange_a lift;
equation
lift.f = 0;
Cd = c_over_l*lift.s;
// Cd = 0.0;
end Valve;
Valvetrain cam
Information
This is an idealized cam model that computes a cam profile from the valve timing (opening and closing) and the
maximum lift.
Parameters
| Name | Default | Description |
| vo | 40 | Valve Open [deg] |
| vc | 205 | Valve Close [deg] |
| max_lift | 0.012 | Maximum valve lift [m] |
Modelica definition
model Cam "Valvetrain cam"
parameter Types.Degrees vo=40 "Valve Open";
parameter Types.Degrees vc=205 "Valve Close";
parameter Modelica.SIunits.Length max_lift=0.012
"Maximum valve lift";
Types.Degrees local_ca "Local camshaft angle";
protected
parameter Real norm=1.0/(vc - vo);
public
Modelica.Mechanics.Rotational.Interfaces.Flange_a camshaft;
Modelica.Mechanics.Translational.Interfaces.Flange_a valve_lift;
equation
assert(vc > vo + 20, "Invalid cam timings");
camshaft.tau = 0;
valve_lift.s = if (local_ca < vo or local_ca > vc) then 0.0 else
max_lift*Modelica.Math.sin((local_ca - vo)*norm*Modelica.Constants.PI)^2;
local_ca = mod(camshaft.phi*180.0/Modelica.Constants.PI, 360);
end Cam;
Parameters
| Name | Default | Description |
| cp | 1039 | Specific heat capacity at constant pressure [J/(kg.K)] |
| mole_weight | 0.028964 | Molecular weight [kg/mol] |
Modelica definition
replaceable model PropertyModel = GasProperties.SimpleAirProperties;
Computes combustion chamber volume as a function of piston position
Information
This model is used to compute the combustion chamber volume as a function
of piston position. The has connectors for providing engine geometry
characteristics and the piston position and computes the chamber volume
as an output signal.
Note that this model does not really interact with the piston because it
does not result in any force being applied to the piston. It is important
when creating models that are "read-only" for a given domain (e.g. the
translational domain in the case of the piston position) that a zero
contribution be provided for any flow variables.
Modelica definition
model ChamberVolume
"Computes combustion chamber volume as a function of piston position"
Modelica.Mechanics.Translational.Interfaces.Flange_a piston;
Modelica.Blocks.Interfaces.OutPort volume(final n=1);
Interfaces.EngineGeometryRequired geom;
equation
volume.signal[1] = geom.Vc + geom.Ap*piston.s;
piston.f = 0;
end ChamberVolume;
Isentropic flow restriction
Information
This is a base model for other models which model isentropic flow (e.g. throttles, engine valves)
Parameters
| Name | Default | Description |
| Aref | | Reference Area [m2] |
Modelica definition
model Orifice "Isentropic flow restriction"
parameter Modelica.SIunits.Area Aref "Reference Area";
protected
Modelica.SIunits.MassFlowRate mdot "Flow from 'a' to 'b'";
Modelica.SIunits.SpecificEnthalpy h "Upstream enthalpy";
Modelica.SIunits.SpecificEnthalpy gamma "Upstream gamma";
Real pratio "Pressure ratio";
Real Cd "Discharge Coefficient";
public
Interfaces.Gas a;
Interfaces.Gas b;
protected
PropertyModel a_props(T=a.T, P=a.P);
PropertyModel b_props(T=b.T, P=b.P);
equation
a.mdot = mdot;
b.mdot = -mdot;
a.q = mdot*h;
b.q = -mdot*h;
if noEvent(a.P > b.P) then
h = a_props.h;
gamma = a_props.gamma;
pratio = b.P/a.P;
if (pratio <= (2.0/(gamma + 1.0))^(gamma/(gamma - 1.0))) then
mdot = Cd*Aref*a.P/((Modelica.Constants.R/a_props.mw)*a.T)^0.5*gamma^0.5
*(2.0/(gamma + 1.0))^((gamma + 1.0)/(2.0*(gamma - 1.0)));
else
mdot = Cd*Aref*a.P/((Modelica.Constants.R/a_props.mw)*a.T)^0.5*pratio^(
1.0/gamma)*(2.0*gamma/(gamma - 1.0)*(1.0 - pratio^((gamma - 1.0)/gamma)
))^0.5;
end if;
else
h = b_props.h;
gamma = b_props.gamma;
pratio = a.P/b.P;
if (pratio <= (2.0/(gamma + 1.0))^(gamma/(gamma - 1.0))) then
mdot = -Cd*Aref*b.P/((Modelica.Constants.R/b_props.mw)*b.T)^0.5*gamma^
0.5*(2.0/(gamma + 1.0))^((gamma + 1.0)/(2.0*(gamma - 1.0)));
else
mdot = -Cd*Aref*b.P/((Modelica.Constants.R/b_props.mw)*b.T)^0.5*pratio^(
1.0/gamma)*(2.0*gamma/(gamma - 1.0)*(1.0 - pratio^((gamma - 1.0)/gamma)
))^0.5;
end if;
end if;
end Orifice;
Orifice with throttle plate
Information
A very simple engine throttle. The input signal is the throttle angle.
Parameters
| Name | Default | Description |
| dia | 0.1 | Throttle diameter [m] |
Modelica definition
model Throttle "Orifice with throttle plate"
parameter Modelica.SIunits.Diameter dia=0.1 "Throttle diameter";
extends Orifice(final Aref=Modelica.Constants.PI*(dia/2)^2);
Modelica.Blocks.Interfaces.InPort throttle_angle(final n=1)
"Throttle Angle [deg]";
equation
Cd = Modelica.Math.sin(throttle_angle.signal[1]*Modelica.Constants.PI/180)^2
;
end Throttle;
A massless piston
Information
This piston is used to translate pressure inside the cylinder into force (presumably applied to the crank slider mechanism).
This piston has no mass.
Parameters
| Name | Default | Description |
| Pcc | 101800 | Crankcase pressure [Pa] |
Modelica definition
model MasslessPiston "A massless piston"
parameter Modelica.SIunits.Pressure Pcc=101800
"Crankcase pressure";
Interfaces.EngineGeometryRequired geom;
Interfaces.Gas chamber;
Modelica.Mechanics.Translational.Interfaces.Flange_a piston;
equation
piston.f = geom.Ap*(chamber.P - Pcc);
chamber.mdot = 0;
chamber.q = 0;
end MasslessPiston;
Angular displacement
Information
Each cylinder is shifted on that crank shaft. This model enforces an angular displacement from one flange to another
so that each cylinder can be rigidly connected to the crankshaft by independently shifted.
Parameters
| Name | Default | Description |
| shift | 0 | Shift from crankshaft [deg] |
Modelica definition
model OffsetShaft "Angular displacement"
parameter Types.Degrees shift=0 "Shift from crankshaft";
Types.RPM shaft_speed;
Modelica.Mechanics.Rotational.Interfaces.Flange_a crank;
Modelica.Mechanics.Rotational.Interfaces.Flange_b cyl;
equation
shaft_speed = 30*der(crank.phi)/Modelica.Constants.pi;
crank.tau + cyl.tau = 0;
cyl.phi = crank.phi + shift*Modelica.Constants.PI/180.0;
end OffsetShaft;
A crank slider mechanism
Information
This model represents the crank slider mechanism used to turn translational
force into the rotational torque applied to the crankshaft.
Modelica definition
model CrankSlider "A crank slider mechanism"
protected
Modelica.SIunits.Length d;
Modelica.SIunits.Angle phi;
Real cp;
Real sp;
public
Interfaces.EngineGeometryRequired geom;
Modelica.Mechanics.Rotational.Interfaces.Flange_a crank;
Modelica.Mechanics.Translational.Interfaces.Flange_a piston;
equation
assert(geom.conrod > geom.crank,
"Connecting rod length greater than crank length");
cp = Modelica.Math.cos(phi);
sp = Modelica.Math.sin(phi);
phi = crank.phi;
d = sqrt(geom.conrod^2 - (geom.crank*sp)^2);
piston.s = (geom.crank + geom.conrod) - (geom.crank*cp + sqrt(geom.conrod^2
- geom.crank^2*sp^2));
crank.tau = piston.f*(sp*geom.crank + cp*sp*geom.crank^2/sqrt(d));
end CrankSlider;
Spark plug control
Information
This model triggers the firing of the spark plug when the piston reaches a prescribed number of degrees
before top dead center of the compression/combustion strokes. A real spark control strategy would allow
the spark strategy to change as engine conditions changed but this model just assumes a fixed
"spark advance".
Parameters
| Name | Default | Description |
| spark_advance | 20 | Before top-dead-center (TDC) [deg] |
Modelica definition
model SparkControl "Spark plug control"
parameter Types.Degrees spark_advance=20
"Before top-dead-center (TDC)";
protected
Types.Degrees cur_pos;
Types.Degrees next_spark;
public
Modelica.Blocks.Interfaces.BooleanOutPort spark;
Modelica.Mechanics.Rotational.Interfaces.Flange_a crank;
equation
crank.tau = 0;
cur_pos = crank.phi*180/Modelica.Constants.PI;
algorithm
when initial() then
next_spark := -spark_advance;
while (next_spark < cur_pos) loop
next_spark := next_spark + 720;
end while;
end when;
spark.signal[1] := cur_pos > next_spark;
when spark.signal[1] then
next_spark := next_spark + 720;
end when;
end SparkControl;
Engine timing belt
Information
A timing belt that makes sure the camshaft spins at half the frequency as the crank shaft.
Modelica definition
model TimingBelt "Engine timing belt"
Modelica.Mechanics.Rotational.Interfaces.Flange_a crankshaft;
Modelica.Mechanics.Rotational.Interfaces.Flange_b camshaft;
equation
2*camshaft.phi = crankshaft.phi;
camshaft.tau = 2*crankshaft.tau;
end TimingBelt;
Simple combustion model
Information
This is a simplified combustion models. This
model works by computing an instantaneous heat
release (i.e. energy given off as a result
of combustion) based on an idealized combustion
transient. The duration of the burning is
typically determined by the motion of the
fuel/air mixture inside the cylinder but in
the case of this model it is assumed that
the burn duration is a fixed parameter.
Parameters
| Name | Default | Description |
| lhv | 44e+6 | Lower heating value [J/kg] |
| afr | 14.6 | Stoichiometric Air/Fuel Ratio |
| burn_duration | 60 | Duration of combustion [deg] |
Modelica definition
model Combustion "Simple combustion model"
parameter Modelica.SIunits.SpecificEnergy lhv=44e+6
"Lower heating value";
parameter Real afr=14.6 "Stoichiometric Air/Fuel Ratio";
parameter Types.Degrees burn_duration=60 "Duration of combustion";
protected
Modelica.SIunits.Energy amplitude;
Modelica.SIunits.AngularVelocity w;
Real dps;
Modelica.SIunits.Time start_burn(start=-1);
Modelica.SIunits.Time end_burn(start=-1);
Boolean burning(start=false);
Real tmp;
public
Interfaces.Gas cylinder;
Modelica.Blocks.Interfaces.InPort mass(final n=1);
Modelica.Blocks.Interfaces.BooleanInPort start(final n=1);
Modelica.Mechanics.Rotational.Interfaces.Flange_a crank;
equation
assert(burn_duration > 1, "Invalid burn duration");
cylinder.mdot = 0;
cylinder.q = if (burning) then -amplitude*Modelica.Math.sin((time -
start_burn)/(end_burn - start_burn)*Modelica.Constants.PI)^2 else 0.0;
der(tmp) = cylinder.q;
w = der(crank.phi);
dps = w*180/Modelica.Constants.PI;
crank.tau = 0;
algorithm
when start.signal[1] then
start_burn := time;
end_burn := time + (burn_duration/dps);
amplitude := lhv*(mass.signal[1]/(afr + 1))*2.0*dps/burn_duration;
burning := true;
end when;
when time >= end_burn then
burning := false;
end when;
end Combustion;
Parameters
| Name | Default | Description |
| spark_advance | | Spark advance [deg] |
| burn_duration | | Burn Duration [deg] |
| evo | 40 | Exhaust Valve Opening [deg] |
| ivo | 150 | Intake Valve Opening [deg] |
| evc | 205 | Exhaust Valve Closing [deg] |
| ivc | 310 | Intake Valve Closing [deg] |
| crank_shift | 0 | Crankshaft Shift [deg] |
| ivd | 0.032 | Intake Valve Diameter [m] |
| evd | 0.028 | Exhaust Valve Diameter [m] |
Modelica definition
replaceable model CylinderType = IndividualCylinder extends Interfaces.Cylinder;
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