Less Emis­sions - More Flex­i­bil­i­ty - In­creased Plant Avail­abil­i­ty

WE re­search for your tech­no­log­i­cal ben­e­fit

Re­search has al­ways played a major role in the his­to­ry of the IfTA GmbH. It makes a de­ci­sive con­tri­bu­tion to our com­pa­ny's suc­cess, as find­ings from our re­search are al­ways in­cor­po­rat­ed into the de­vel­op­ment of new IfTA prod­ucts and so­lu­tions.

Above all, re­search at IfTA GmbH pro­vides for green­er en­er­gy gen­er­a­tion: the focus of our re­search is on com­bus­tion dy­nam­ics, which typ­i­cal­ly occur in gas tur­bines. Gas tur­bines are used e.g. for power gen­er­a­tion and will con­tin­ue to be need­ed there in the fu­ture, as power gen­er­a­tion from re­new­able en­er­gy sources alone is not suf­fi­cient or flex­i­ble enough to cover peak loads and times when there is no solar/wind power gen­er­a­tion avail­able. Thus, new tech­nolo­gies and de­vel­op­ments that make con­ven­tion­al en­er­gy gen­er­a­tion more ef­fec­tive and en­vi­ron­men­tal­ly friend­ly are also cru­cial for green en­er­gy. Ex­am­ples for such new tech­nolo­gies are big data anal­y­sis or hy­dro­gen com­bus­tion.

This is our daily mo­ti­va­tion: With our re­search and our prod­uct so­lu­tions around os­cil­la­tion mea­sure­ments, we en­sure a sig­nif­i­cant re­duc­tion of emis­sions and thus con­trib­ute to the pro­tec­tion of our plan­et.

IfTA GmbH Re­search Pro­jects

picture showing artificial intelligence - interface - human hand

AI for Pre­dic­tive Main­te­nance in Gas Tur­bines

 

Ef­fi­cient Ma­chine Oper­a­tion Thanks to AI

The project uses ar­ti­fi­cial in­tel­li­gence for mon­i­tor­ing and pre­dic­tive main­te­nance in com­bus­tion cham­bers of gas tur­bines.  

Achieve­ments: By using AI anom­alies can be de­tect­ed at an early stage and dam­age and shut­downs can be pre­vent­ed by means of pre­dic­tive main­te­nance.

Co­op­er­a­tion project in close co­op­er­a­tion withKI-Trans­fer Plusas well asap­plied AI,  fund­ed by the Bavar­i­an Min­istry for Digi­tial Af­fairs 

Run­time: March 2021 - March 2022

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SensE: Com­bin­ing Edge and Cloud

 

Sen­sors at the Edge

The goal of the project is to process sen­sor data from the edge and cloud in a col­lab­o­ra­tive man­ner so that the ad­van­tages (low la­ten­cy at the edge, high pro­cess­ing power in the cloud) of the re­spec­tive sys­tems can be uti­lized.

Co­op­er­a­tion project with the Tech­ni­cal Univer­si­ty Mu­nich, Chair of Com­put­er Ar­chi­tec­ture & Par­al­lel Sys­tems, spon­sored by the Bavar­i­an Re­search Foun­da­tion

Run­time: May 2020 - April 2023

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Combustion of hydrogen for energy production

POLKA: Com­bus­tion of Hy­dro­gen for En­er­gy Pro­duc­tion

POLlu­tions-Know-how and -Abate­ment

The cur­rent POLKA project deals with com­bus­tion of hy­dro­gen, es­pe­cial­ly with ref­er­ence to ther­moa­cous­tic in­sta­bil­i­ties and flash­back.

Co­op­er­a­tion project with sev­er­al Univer­si­ties and or­ga­ni­za­tions under the man­age­ment of the Univer­si­ty of Keele, Unit­ed King­dom

Run­time: 2019 - 2023

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Optimization of Gas Power Plants by means of Big Data

TurbO: Op­ti­miza­tion of Gas Power Plants by Means of Big Data

Gas Turbine Opti­miza­tion Using Big Data and Ma­chine Learn­ing

Within the scope of the TurbO project it is being in­ves­ti­gat­ed in which form sen­sor data can con­trib­ute to the op­ti­miza­tion of plants and the de­vel­op­ment of gas tur­bines. The focus is put on the anal­y­sis of so far un­used fre­quen­cy spec­tra, the anal­y­sis of larg­er time slots and the com­par­i­son of gas tur­bines of the same type with­in one fleet.

Achieve­ments: A new data stor­age con­cept for the IfTA DataHub

Co­op­er­a­tion project with the Tech­ni­cal Univer­si­ty Mu­nich, Lehrstuhl für Rech­ner­ar­chitek­tur & Par­al­lele Sys­teme, spon­sored by the Bay­erische Forschungss­tiftung

Run­time: Septem­ber 2017 - Au­gust 2020

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Reduction of emissions in combustion systems

TANGO: Re­duc­tion of Emis­sions in Com­bus­tion Plants

Ther­moa­cous­tic and Aeroa­cous­tic Non­lin­ear­i­ties in Green Com­bus­tors with Ori­fice Struc­tures

The scope of the TANGO project com­prised ther­moa­cous­tics and aeroa­cous­tics in mod­ern com­bus­tion sys­tems with the goal of emis­sion re­duc­tion.

Achieve­ments: Pa­tent for early warn­ing sys­tem IfTA PreCur­sor 

Co­op­er­a­tion project with sev­er­al Univer­si­ties and or­ga­ni­za­tions under the man­age­ment of the Univer­si­ty of Keele, Unit­ed King­dom

Run­time: Novem­ber 2012 - Novem­ber 2016

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LIMOUSINE: Op­ti­mized Plant Avail­abil­i­ty of Gas Tur­bines

Limit Cy­cles of Ther­moacoustic Os­cil­la­tions in Gas Turbine Com­bus­tors

The LIMOUSINE project fo­cused on pre­dic­tions and char­ac­ter­is­tics of ther­moa­cous­tic limit cycle be­hav­iour in gas tur­bine com­bus­tors.

Achieve­ments: Bet­ter com­pre­hen­sion of com­bus­tion dy­nam­ics and ac­tive sup­pres­sion of in­sta­bil­i­ties

Co­op­er­a­tion project with sev­er­al Univer­si­ties and or­ga­ni­za­tions under the man­age­ment of the Univer­si­ty of Twente, Nether­lands

Run­time: Oc­to­ber  2008 - Oc­to­ber 2012

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AI for Pre­dic­tive Main­te­nance of Gas Tur­bines

How can we Avoid Gas Tur­bine down­times? 

In the power plant sec­tor spon­ta­neous­ly oc­cur­ring fail­ures in gas tur­bines can re­sult in major cost and in ex­treme cases even af­fect se­cu­ri­ty of sup­ply. In ad­di­tion to the ac­tu­al re­pair and spare parts costs, there is also a pro­duc­tion loss and even penal­ties in some cases. In order to pre­vent this, IfTA is de­vel­op­ing an anom­aly de­tec­tion sys­tem based on ar­ti­fi­cial in­tel­li­gence that en­ables pre­dic­tive main­te­nance. In this way, the goal is to avoid ex­pen­sive down­time.

 

 

 

 

 

Pro­ject Tasks and Re­search con­tent at IfTA

The project in­ves­ti­gates pre­dic­tive main­te­nance of gas tur­bines using ar­ti­fi­cial in­tel­li­gence. The ap­proach is that AI-based al­go­rithms search through the gas tur­bine's op­er­at­ing in­for­ma­tion and sen­sor data for de­vi­a­tions from nor­mal op­er­at­ing be­hav­ior, so-called anom­alies. Con­trary to es­tab­lished sys­tems, there is no need to know how par­tic­u­lar de­fects are in­di­cat­ed in the mea­sure­ment data. As a re­sult, com­plete­ly new and so far un­known de­fects can be de­tect­ed, al­low­ing a more com­pre­hen­sive and re­li­able mon­i­tor­ing. In ad­di­tion, it is pos­si­ble to de­tect de­fects at an early stage. This en­ables early ac­tion and can thus avoid un­planned emer­gen­cy shut­downs and the as­so­ci­at­ed high costs.

 

 

Re­search Team and Co­op­er­a­tion Part­ners

The project was car­ried out as part of the AI Trans­fer Plus project and fund­ed by the Bavar­i­an State Min­istry for Dig­i­tal Af­fairs. There was close co­op­er­a­tion with ap­pliedAI - the Ini­tia­tive for Ap­plied Ar­ti­fi­cial In­tel­li­gence. The Stadtwerke München SWM pro­vid­ed us with record­ed mea­sure­ment data for this project.

Re­sults of the re­search project

The project was a great suc­cess for IfTA, as by using AI any anom­alies can be de­tect­ed sig­nif­i­cant­ly ear­li­er. Our ex­per­tise in the field of ar­ti­fi­cial in­tel­li­gence could be ex­pand­ed. In ad­di­tion, a first pro­to­type (as of Au­gust 2022) for an­no­ma­ly de­tec­tion using AI shows very good re­sults.

Watch the video "AI in use at IfTA" on youTube
(to see English sub­ti­tles click on the set­tings at the right cor­ner of the video and choose English sub­ti­tles)  

 

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SENSE - Com­bin­ing Edge and Cloud

How to han­dle big data in the fu­ture?

The con­tin­u­ous avail­abil­i­ty of sys­tem-crit­i­cal in­fra­struc­ture is es­sen­tial for to­day’s so­ci­ety. En­er­gy and drink­ing water sup­ply sys­tems, mo­bil­i­ty and com­mu­ni­ca­tions in­fra­struc­ture, and many oth­ers are there­fore per­ma­nent­ly mon­i­tored with sen­sors. Enor­mous vol­umes of data that are gen­er­at­ed. An­a­lyz­ing it re­quires in­tel­li­gent use of the avail­able com­put­ing re­sources both on the site - close to the sen­sors - at the “Edge” - and in High Per­for­mance Com­put­ing sys­tems or in the “Cloud”.

 

 

 

Pro­ject Tasks and Re­search con­tent at IfTA

The aim of SensE is to ef­fi­cient­ly com­bine all avail­able re­sources for sen­sor data pro­cess­ing from the edge to the Cloud in a col­lab­o­ra­tive fash­ion. Its par­tic­u­lar em­pha­sis is on scal­a­bil­i­ty and adap­tiv­i­ty of the sen­sor pro­cess­ing sys­tem through the in­tel­li­gent cou­pling of sys­tem com­po­nents and data.

In SensE, we aim to im­ple­ment this vi­sion: small and en­er­gy-sav­ing sys­tems, on site, process and an­a­lyze the lo­cal­ly avail­able sen­sor data streams and use it in local process con­trol loops. At the same time, the lo­cal­ly gen­er­at­ed anal­y­sis re­sults and a re­duced (in­tel­li­gent­ly-se­lect­ed) sub­set of the data are for­ward­ed to the Cloud or to a High Per­for­mance Com­put­ing sys­tem for more com­plex analy­ses and com­bine data and mod­els from mul­ti­ple sys­tems.

SensE aims to in­ves­ti­gate the cou­pling of the re­sources, mod­els, and data, and through this in­te­gra­tion, an op­ti­mized sen­sor pro­cess­ing sys­tem can be de­vel­oped. This sys­tem can be used on the com­put­er sys­tems at the edge to im­prove the mon­i­tor­ing sys­tems and op­ti­mize the local con­trol loops.

 

 

Re­search Team and Co­op­er­a­tion Part­ners

The project is car­ried out in close co­op­er­a­tion with our part­ner, the Chair of Com­put­er Ar­chi­tec­ture & Par­al­lel Sys­tems at the Tech­ni­cal Univer­si­ty of Mu­nich, and is fund­ed by the Bavar­i­an Re­search Foun­da­tion.

From the side of the in­dus­try, the Stadtwerke München SWM GmbH in Ger­many con­trib­ute to ob­tain prac­ti­cal re­sults.

Re­sult­ing Publi­ca­tions

POLKA - Com­bus­tion of Hy­dro­gen for En­er­gy Pro­duc­tion

 Com­bus­tion of hy­dro­gen - the fu­ture of en­er­gy pro­duc­tion?

The com­bus­tion of hy­dro­gen has the po­ten­tial to play a key role in the en­er­gy tran­si­tion. Thus, in times of over­pro­duc­tion, hy­dro­gen can be pro­duced using re­new­able en­er­gy sources and then in times of deficit, it can be burned in ex­ist­ing gas-fired power plants. One way to ac­com­plish this is to add hy­dro­gen to con­ven­tion­al fuels. How­ev­er, de­pend­ing on the ratio, this has sig­nif­i­cant im­pli­ca­tions for the com­bus­tion process. Com­pared to nat­u­ral gas, for ex­am­ple, hy­dro­gen has a lower en­er­gy den­si­ty and a sig­nif­i­cant­ly high­er flame speed. This re­sults in new chal­lenges, par­tic­u­lar­ly with re­spect to ther­moa­cous­tic sta­bil­i­ty and flame flash­back, which will be in­ves­ti­gat­ed as part of the project.

 

Pro­ject tasks and re­search con­tent at IfTA

An im­por­tant project goal is to fur­ther de­vel­op the early de­tec­tion of com­bus­tion in­sta­bil­i­ties in gas tur­bines. Ex­ist­ing sys­tems, such as the IfTA PreCur­sor, are mode-based and thus con­cen­trate on mon­i­tor­ing in­di­vid­u­al, known com­bus­tion cham­ber modes. If hy­dro­gen is now added to the fuel in dif­fer­ent con­cen­tra­tions, it is dif­fi­cult to pre­dict what in­flu­ence this will have on the rel­e­vant modes. Depend­ing on the gas tur­bine or hy­dro­gen con­tent, a dif­fer­ent pic­ture is drawn. For this rea­son, re­search fo­cus­es on new early de­tec­tion meth­ods that do not re­quire any pre­vi­ous knowl­edge of the re­spec­tive ther­moa­cous­tic eigen­modes. These re­sults should even­tu­al­ly lead to an ex­tend­ed ver­sion of the IfTA PreCur­sor.

This ob­jec­tive re­quires the de­vel­op­ment of re­source-ef­fi­cient phys­i­cal mod­els as well as the im­ple­men­ta­tion of new al­go­rithms for real-time sig­nal pro­cess­ing. For the de­vel­op­ment and test­ing of a pro­to­type, the al­ready ex­ist­ing LIMOUSINE lab­o­ra­to­ry burn­er will be used. If things pro­ceed as planned, the ad­vanced PreCur­sor for early de­tec­tion of com­bus­tion in­sta­bil­i­ties can al­ready be im­ple­ment­ed in an in­dus­tri­al-size com­bus­tor with­in the POLKA project du­ra­tion.

Re­search Team and co­op­er­a­tion part­ners

The re­search project con­sists of an in­ter­na­tion­al con­sor­tium of uni­ver­si­ties and com­pa­nies:

Keele Univer­si­ty, Keele, Unit­ed King­dom, TUM - Tech­ni­cal Univer­si­ty Mu­nich, Mu­nich, Ger­many, KTH Royal In­sti­tute of Tech­nol­o­gy, Stock­holm, Swe­den, Tech­ni­cal Univer­si­ty Eind­hoven, Eind­hoven, Nether­lands, Univer­si­ty of Gen­o­va, Gen­o­va, Italy, Univer­si­ty of Pisa, Pisa, Italy, Siemens En­er­gy In­dus­try Soft­wareNV, Leu­ven, Bel­gium, AE - An­sal­do En­er­gia SpA, Gen­o­va, Italy, BEK - Bekaert Com­bus­tion Tech­nol­o­gy BV, Assen, Nether­lands,  IfTA - In­ge­nieur­büro für Ther­moakustik, Puch­heim, Ger­many

Re­sults of the re­search project

This project is cur­rent­ly in progress and re­sults can­not be pre­sent­ed yet. The goal of the re­search project is to fur­ther de­vel­op the model-based iden­ti­fi­ca­tion of ther­moa­cous­tic modes in gas tur­bines.

 

TurbO - Op­ti­miza­tion of Gas Power Plants with the Help of Big Data

Data anal­y­sis for more ef­fi­cien­cy in power sup­ply

Gas tur­bines con­trib­ute sig­nif­i­cant­ly to the power sup­ply. Due to their quick op­er­a­tional readi­ness, they are ide­al­ly suit­ed to cover peak loads. How­ev­er, for ef­fi­cient and safe op­er­a­tion, such dy­nam­ic op­er­at­ing con­di­tions re­quire a spe­cial­ly op­ti­mized ma­chine de­sign along with the ap­pro­pri­ate op­er­at­ing strate­gies. Both can only be de­vel­oped and val­i­dat­ed on the basis of a solid data­base of op­er­at­ing data, which ul­ti­mate­ly re­quires con­tin­u­ous mon­i­tor­ing of the ma­chine sta­tus and stor­age of the data col­lect­ed in the process.

So far, one has re­lied on op­er­at­ing data with rel­a­tive­ly low tem­po­ral res­o­lu­tion com­pared to the de­ci­sive phys­i­cal pro­cess­es in the com­bus­tion cham­ber. High-res­o­lu­tion dy­nam­ic sen­sor data, such as from al­ter­nat­ing pres­sure sen­sors in the com­bus­tion cham­ber or ac­cel­er­a­tion sen­sors on bear­ings, can cur­rent­ly not be used due to the im­mense vol­ume of data. Their col­lec­tion and eval­u­a­tion is sim­ply not pos­si­ble with the cur­rent­ly es­tab­lished meth­ods. This deficit of­fers great po­ten­tial for op­ti­miza­tion, which will be in­ves­ti­gat­ed as part of this re­search project.

 

 

Pro­ject Tasks and Re­search Con­tent at IFTA

As a first step, the re­search project will in­ves­ti­gate pos­si­bil­i­ties for stor­ing gas tur­bine op­er­at­ing data ap­pro­pri­ate­ly. The focus here is on find­ing a stor­age for­mat that al­lows the data to be pro­cessed as ef­fi­cient­ly as pos­si­ble. In order to val­i­date the de­vel­oped con­cepts, about 300 TByte of gas tur­bine data are avail­able, which have been record­ed 24/7 with high res­o­lu­tion over years by IfTA Ar­gusOMDS mon­i­tor­ing sys­tems.

Based on the newly dis­cov­ered stor­age con­cept, in a sec­ond step in­no­va­tive anal­y­sis al­go­rithms will be in­ves­ti­gat­ed with the aim of gain­ing new in­sights into ma­chine op­ti­miza­tion. On the one hand, the gen­er­at­ed data will be (pre-)pro­cessed in real-time in the power plant, and on the other hand, it will be an­a­lyzed in its en­tire­ty on su­per­com­put­ers (e.g. Su­perMUC). In order to present the re­sults to op­er­at­ing en­gi­neers in an easy-to-un­der­stand way, re­search is also done in the field of vi­su­al­iza­tion.

 

Re­search Team and Co­op­er­a­tion Part­ners

The project is car­ried out in close co­op­er­a­tion with our part­ner, the Chair of Com­put­er Ar­chi­tec­ture & Par­al­lel Sys­tems at the Tech­ni­cal Univer­si­ty of Mu­nich, and is fund­ed by the Bavar­i­an Re­search Foun­da­tion.

From the side of the in­dus­try, the Stadtwerke München SWM in Ger­many con­trib­ute to ob­tain prac­ti­cal re­sults.

Re­sults of the re­search project

One suc­cess the team achieved was find­ing a new way to com­press sen­sor data quick­ly and ef­fi­cient­ly in the area of data stor­age. Im­por­tant com­po­nents of these find­ings have al­ready been im­ple­ment­ed in the de­vel­op­ment of the IfTA Da­­taHub soft­ware.


Re­search work Won the Hans Meuer AWARD 2020

The re­search "Time Series Min­ing at Pe­tas­cale Per­for­mance" with­in the TurbO project has won the Hans Meuer Award in June 2020! The Hans Meuer Award hon­ors the most out­stand­ing re­search paper sub­mit­ted to the Re­search Paper Com­mit­tee of In­ter­na­tion­al Su­per­com­put­ing Con­fer­ence.

Re­sult­ing Publi­ca­tions

Tango - Re­duc­tion of Emis­sions in Com­bus­tion Plants

GREEN Com­bus­tion Te­cholo­gies And Noise Con­trol Meth­ods

The aim of this re­search project is to pro­mote the de­vel­op­ment of green and re­li­able com­bus­tion tech­nolo­gies for gas tur­bine power plants. One rea­son for mo­ti­va­tion is the fact that mod­ern gas tur­bines are sus­cep­ti­ble to dan­ger­ous ther­moa­cous­tic in­sta­bil­i­ties due to the pre­mix­ing need­ed to re­duce NOx emis­sions. The other is that gas-fired power plants must be op­er­at­ed with in­creas­ing flex­i­bil­i­ty to com­pen­sate for fluc­tu­at­ing power gen­er­a­tion from re­new­able en­er­gy sources, which makes their de­sign more com­pli­cat­ed.

 

Pro­ject Tasks and re­search Con­tent at IfTA

The sub­pro­ject worked on by IfTA with­in the re­search project com­pris­es the de­vel­op­ment of an early warn­ing sys­tem for ther­moa­cous­tic in­sta­bil­i­ty. The aim is to de­tect in­sta­bil­i­ties de­vel­op­ing in the com­bus­tion cham­ber at an early stage, to warn the gas tur­bine con­troller and in this way to pre­vent the oc­cur­rence of high-am­pli­tude pres­sure os­cil­la­tions. The focus is on can-an­nu­lar com­bus­tion sys­tems, since these are par­tic­u­lar­ly sus­cep­ti­ble to low-fre­quen­cy, ther­moa­cous­tic cir­cum­fer­en­tial modes.

Re­search Team and Co­op­er­a­­tion Part­n­ers

The re­search project con­sist­ed of an in­ter­na­tion­al con­sor­tium of Univer­si­ties and com­pa­nies:

Keele Univer­si­ty, Keele, Unit­ed King­dom, Tech­ni­cal Univer­si­ty Eind­hoven, Eind­hoven, Nether­lands, LMS In­ter­na­tion­al NV, Leu­ven, Bel­gium, KTH Kung­li­ga Tekniska Hoegskolan, Teknikri­gen, Swe­den, IITM - In­di­an In­sti­tute of Tech­nol­o­gy Madras, Chen­nai, India, AE - An­sal­do En­er­gia SpA, Gen­o­va, Italy, TUM - Tech­ni­cal Univer­si­ty Mu­nich, Mu­nich, Ger­many, BEK - Bekaert Com­bus­tion Tech­nol­o­gy BV, Assen, Nether­lands, IfTA - In­ge­nieur­büro für Ther­moakustik, Puch­heim, Ger­many

Re­sults of the Re­search Pro­ject

  • Suc­cess­ful in­dus­tri­al im­ple­men­ta­tion of a sys­tem for early de­tec­tion of ther­moa­cous­tic in­sta­bil­i­ties in an­nu­lar com­bus­tion cham­bers
  • The early warn­ing sys­tem IfTA PreCur­­sor was filed for pa­­tent in 2015
  • The re­search work was pub­lished as a dis­­ser­­ta­ti­on in 2021

Li­mou­sine - Op­ti­mized Plant Avail­abilty of Gas Tur­bines

Re­duc­ing gas tur­bine dam­age and down­time

The con­tent of this project are so-called ther­moa­cous­tic limit cy­cles in gas tur­bine com­bus­tors. This phe­nom­e­non in­volves cyclic fluc­tu­a­tions of the com­bus­tor pres­sure, which can reach such high am­pli­tudes that the gas tur­bine can be dam­aged. In order to avoid such cyclic fluc­tu­a­tions, a fun­da­men­tal un­der­stand­ing of the rel­e­vant phys­i­cal pro­cess­es is re­quired.

Pri­mar­i­ly, acous­tics, aero­dy­nam­ics and fluid-struc­ture in­ter­ac­tions need to be in­ves­ti­gat­ed an­a­lyt­i­cal­ly and nu­mer­i­cal­ly. Em­pir­i­cal cor­re­la­tions be­tween op­er­at­ing pa­ram­e­ters or op­er­at­ing con­di­tions and the pres­sure am­pli­tudes that occur also help to iden­ti­fy par­tic­u­lar­ly vul­ner­a­ble op­er­at­ing points. Based on these find­ings, pas­sive and ac­tive cor­rec­tions can be made to the com­bus­tion process to pre­vent the oc­cur­rence of harm­ful limit cy­cles and thus re­duce dam­age and down­times of gas tur­bines.

Pro­ject tasks and re­search con­tent at IFTA

IfTA's aim is to re­duce the ther­moa­cous­tic pres­sure os­cil­la­tions oc­cur­ring in an ex­per­i­men­tal burn­er sys­tem by ac­tive con­trol in­ter­ven­tion. For this pur­pose, an at­mo­spher­ic lab­o­ra­to­ry com­bus­tion cham­ber is con­struct­ed first, which shows strong limit cycle os­cil­la­tions under cer­tain op­er­at­ing con­di­tions. At the same time, a nu­mer­i­cal cal­cu­la­tion model of this setup is gen­er­at­ed and val­i­dat­ed on the basis of the ex­per­i­men­tal data.

Fi­nal­ly, in order to ac­tive­ly in­flu­ence the com­bus­tion process, spe­cial valves are used which allow to con­trol the fuel sup­ply to the com­bus­tion cham­ber. By an­a­lyz­ing the ex­per­i­men­tal and nu­mer­i­cal data, a com­pre­hen­sive un­der­stand­ing of the phys­i­cal pro­cess­es will be gained and, based on this, an op­ti­mized con­troller will be de­signed.

Re­search Team and Co­op­er­a­tion Part­ners

The re­search project con­­sist­ed of an in­­ter­­na­­tion­al con­­sor­tium of Univer­si­ties and com­­pa­nies:

Sulz­er Turbo Ser­vices Venlo B.V., Lomm, Nether­lands, Ansys UK, Ox­ford­shire, Unit­ed King­dom, BRNO Univer­si­ty of Tech­nol­o­gy, Brno, Czech Repub­lic, CERFACS, Toulouse, France, DLR In­sti­tute of Com­bus­tion Tech­nol­o­gy, Stuttgart, Ger­many, Elec­tra­bel, Zwolle, Nether­lands, Im­pe­ri­al Col­lege Lon­don, Unit­ed King­dom, Keele Univer­si­ty, Keele, Unit­ed King­dom, Siemens En­er­gy Power Gen­er­a­tion, Mül­heim a. d. Ruhr, Ger­many, Univer­si­ty of Zaragoza, Zaragoza, Spain, Univer­si­ty of Twente, En­schede, Nether­lands, IfTA - In­ge­nieur­büro für Ther­moakustik, Puch­heim, Ger­many

Re­sults of the Re­search Pro­ject

  • Bet­ter un­der­stand­ing of com­bus­tion dy­nam­ics and ac­tive in­sta­bil­i­ty con­trol
  • Char­ac­ter­i­za­tion of the lin­ear acous­tic be­hav­ior of a fuel valve. This acous­tic de­scrip­tion of the valve could be in­te­grat­ed into acous­tic net­work mod­els
  • Deter­mi­na­tion of the rel­e­vant pa­ram­e­ters and phe­nom­e­na for the sta­bi­liza­tion of the ex­per­i­men­tal com­bus­tion cham­ber with the aid of fuel fluc­tu­a­tions

Re­sult­ing pub­li­ca­tions