Fuel Flexible Prediction Model for T100 Gas Turbine
(2010) In ISRN LUTMDN/TMHP-10/5210--SEDepartment of Energy Sciences
- Abstract
- To be able to predict the behaviour of the things around us is valuable asset, whether it is the stock prices
or the train arrival, a chance to know before it is happening is always beneficial. The computer has taken
the prediction of behaviour to a higher level. From reading tea leaves in a cup, to really look into large
amounts of data and with previous answers find a good approximation to what will happen.
This report will explain how such operation can be done. It will describe a program designed for an
electrical power generating gas turbine. With a large number of nested equations, which represents the
components in this machine, a prediction of what will happen when the circumstances decline from its
ideal conditions, is... (More) - To be able to predict the behaviour of the things around us is valuable asset, whether it is the stock prices
or the train arrival, a chance to know before it is happening is always beneficial. The computer has taken
the prediction of behaviour to a higher level. From reading tea leaves in a cup, to really look into large
amounts of data and with previous answers find a good approximation to what will happen.
This report will explain how such operation can be done. It will describe a program designed for an
electrical power generating gas turbine. With a large number of nested equations, which represents the
components in this machine, a prediction of what will happen when the circumstances decline from its
ideal conditions, is found. This feature is of great importance when understanding why such complex
machine acts as it does.
When making an advanced simulation program like this, the focus cannot only be on getting a good
approximation. If no one can run the program or understand the results, it has been done for nothing.
Words as “user friendly” and “robust” might sound like clichés, but they cannot be over emphasised.
The way of completing this master thesis has gone through three phases; finding the right answers, making
it stable and making it understandable. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/2541208
- author
- Nyberg, Björn
- supervisor
- organization
- year
- 2010
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- generating electrical power gas turbine simulation
- publication/series
- ISRN LUTMDN/TMHP-10/5210--SE
- language
- English
- id
- 2541208
- date added to LUP
- 2012-05-16 13:32:33
- date last changed
- 2012-05-16 13:32:33
@misc{2541208, abstract = {{To be able to predict the behaviour of the things around us is valuable asset, whether it is the stock prices or the train arrival, a chance to know before it is happening is always beneficial. The computer has taken the prediction of behaviour to a higher level. From reading tea leaves in a cup, to really look into large amounts of data and with previous answers find a good approximation to what will happen. This report will explain how such operation can be done. It will describe a program designed for an electrical power generating gas turbine. With a large number of nested equations, which represents the components in this machine, a prediction of what will happen when the circumstances decline from its ideal conditions, is found. This feature is of great importance when understanding why such complex machine acts as it does. When making an advanced simulation program like this, the focus cannot only be on getting a good approximation. If no one can run the program or understand the results, it has been done for nothing. Words as “user friendly” and “robust” might sound like clichés, but they cannot be over emphasised. The way of completing this master thesis has gone through three phases; finding the right answers, making it stable and making it understandable.}}, author = {{Nyberg, Björn}}, language = {{eng}}, note = {{Student Paper}}, series = {{ISRN LUTMDN/TMHP-10/5210--SE}}, title = {{Fuel Flexible Prediction Model for T100 Gas Turbine}}, year = {{2010}}, }