SlideShare ist ein Scribd-Unternehmen logo
1 von 4
Downloaden Sie, um offline zu lesen
COMPENSATION OF BEAM INDUCED EFFECTS IN LHC CRYOGENIC
SYSTEMS
B. Bradu, E. Rogez, G. Iadarola, E. Blanco Viñuela, G. Ferlin, A. Tovar González,
B. Fernández Adiego, P. Plutecki, CERN, Geneva, Switzerland
Abstract
This paper presents the different control strategies de-
ployed in the LHC cryogenic system in order to compensate
the beam induced effects in real-time. The LHC beam is
inducing important heat loads along the 27 km of beam
screens due to synchrotron radiation, image current and elec-
tron clouds. These dynamic heat loads significantly disturb
the cryogenic plants and automatic compensation is manda-
tory to operate the LHC at full energy. The LHC beam
screens must be maintained in an acceptable temperature
range around 20 K to ensure a good beam vacuum, espe-
cially during beam injections and energy ramping where
the dynamic responses of cryogenic systems cannot be man-
aged with conventional feedback control techniques. Con-
sequently, several control strategies such as feed-forward
compensation have been developed and deployed success-
fully on the machine during 2015 where the beam induced
heat loads are forecast in real-time to anticipate their future
effects on cryogenic systems. All these developments have
been first entirely modelled and simulated dynamically in
order to be validated, allowing then a smooth deployment
during the LHC operation.
INTRODUCTION
In most superconducting particle accelerators such as the
LHC (Large Hadron Collider) at CERN (European Organisa-
tion for Nuclear Research), the dedicated cryogenic systems
are used to maintain the superconductivity in the magnets
and RF cavities. Nevertheless, particle beams travelling
through the magnets can generate important heat losses in
the beam vacuum chambers and these dynamic heat loads
must be removed as well by the cryogenic systems. For this
reason, the LHC embeds a beam screen within the beam pipe
in order to remove the beam induced effects and to ensure
an ultra-high vacuum [1].
LHC BEAM SCREENS
The LHC beam screens are cooled by supercritical helium
at 3 bar and 4.6 K provided by cryogenic refrigerators
located at the surface. Helium is then heated up to 20 K by
the beam induced effects or by an electrical heater when the
beam is not present. The beam screen temperature has to
be maintained as stable as possible to ensure an ultra high
vacuum in the beam pipe and a stable gaseous helium return
flow to the refrigerators. The temperature is controlled by
a heater at the inlet and by a control valve at the outlet, see
Figure 1. In total, the LHC contains 485 beam screen cooling
loops over the 27 km and a typical loop in the arc measures
53 m (half-cell).
Figure 1: LHC beam screen cooling scheme.
During Run 1 of the LHC in 2010-2012, the deposited
beam screen heat loads (Qdbs) were pretty low, about
0.1 W/m per aperture, corresponding to only 1.7 % of
the total LHC refrigeration capacity at 4.5 K. As particle
bunches were separated by a gap of 50 ns only, these heat
loads were induced by synchrotron radiations and image
current [2]. For LHC Run 2 (2015-2018), the bunch spacing
was reduced to 25 ns in order to get higher beam inten-
sity and thus electron cloud effects become dominant in the
beam screen heat loads [3]. The LHC design report fore-
casts a total beam screen heat load of about 0.8 W/m per
aperture at nominal conditions [4] which represents a signif-
icant part of the installed cryogenic capacity at 4.5 K (about
12 %). As these heat loads are dynamic and can evolve
rapidly compared to the cryogenic system inertia, automatic
compensation is mandatory in the cryogenic control system.
BEAM SCREENS TEMPERATURE
CONTROL
The beam screen inlet temperature is maintained above
a minimum temperature using the electrical heater at the
entrance of the circuit to avoid thermo-hydraulic oscilla-
tions and the outlet temperature is controlled at 20 K using
the control valve. To perform these controls, classical PID
(Proportional-Integral-Derivative) controllers are used. Un-
fortunately, the time constants and delays of the cryogenic
systems regarding the beam induced heat load dynamics are
not suitable for such feedback loops and the PID controllers
cannot reject these disturbances efficiently [5].
To improve the control of the temperature ensuring its
stability, feed-forward actions on the valve and on the heater
have been added to prevent the beam disturbances. These
feed-forward contributions are calculated from the available
beam information (energy, intensities, number of bunches
Proceedings of IPAC2016, Busan, Korea TUPMB048
07 Accelerator Technology
T13 Cryogenics
ISBN 978-3-95450-147-2
1205
Copyright©2016CC-BY-3.0andbytherespectiveauthors
and bunch length means) to estimate the beam induced heat
loads and to position the valve and the heater close to their fi-
nal expected values, see Figure 2 where the control scheme is
represented, embedding the feed-forward transfer functions
FF1 and FF2. Moreover, the inlet temperature set-point is
switched between 13 K and 6 K according to the presence
of the beam to save energy.
Figure 2: LHC beam screen control scheme.
This control scheme has been validated using dynamic
simulations performed with the modeling and simulation
software EcosimPro and the cryogenic library CRYOLIB [6],
see Figure 3, where two dynamic simulations are compared.
Once validated in simulation, this improved control scheme
has been successfully deployed in 2015 throughout the en-
tire LHC cryogenic control system. Nevertheless, to have
these feed-forward actions working in an efficient way, it is
necessary to correctly estimate the deposited heat load on
the beam screens in real-time, otherwise a temperature over-
shoot or a sub-cooling of the beam screens can be observed.
Figure 3: Simulations of the beam screen cryogenic circuit
during a beam injection generating 1.2 W/m per aperture
with and without Feed-Forward actions (FF).
DEPOSITED HEAT LOAD ESTIMATIONS
IN BEAM SCREENS
Computation of beam screen heat loads
The deposited heat load on the beam screens Qdbs can
be computed as the sum of three main induced heat loads
contributions as represented in equation (1): synchrotron
radiations Qsr , image current Qic and electron clouds Qec.
Qdbs = Qsr + Qic + Qec (1)
Synchrotron radiation and image current contributions
can be estimated by scaling their design values with the
beam parameters [7] as represented in equations (2) and (3).
The Table 1 describes all constants.
Qsr = ¯Qsr · L ·
E
¯E
4
·
Nb
¯Nb
·
nb
¯nb
(2)
Qic = ¯Qic ·L·
0.6 · E + 2800
¯E
·
Nb
¯Nb
2
·
nb
¯nb
·
σ
¯σ
p
(3)
Concerning the electron cloud contribution Qec, there is
no such simple scaling as it depends on the injection scheme
of the machine (i.e. including the number and length of the
gaps that are present in the bunch train) and on the condition-
ing of the surface where electrons are generated. Moreover,
this term has a tendency to decrease with the running time
of the machine as the beam circulation gradually conditions
the surface.
Nevertheless, the electron cloud heat load increases with
the intensity and the energy of the beam. It is then possible
to establish from measurements along the ring an electron
cloud heat load value per bunch for each part of the machine
at the injection energy (qeci) and at the final energy after the
ramp (qecr ). Finally, the equation (4) was used to model the
electron cloud contribution for each LHC sector. The terms
qeci and qecr are the measured electron cloud heat load per
bunch at the beginning of the run. The terms Kecr and Keci
are tuning parameters to adjust the heat load estimations
according to the conditioning taking place in the machine
along the run: these two terms are equal to 1.0 at the be-
ginning of the run and they are then decreased manually by
operators according to the cleaning effect.
Qec = Keci · qeci
2 · 1 −
E−Ein j
Er amp −Ein j
· nb · Nb
¯Nb
+Kecr · qecr
2 ·
E−Ein j
Er amp −Ein j
· nb · Nb
¯Nb
(4)
Comparisons with real measurements
During Run 1, the synchrotron radiation and the image
current contributions could easily be calculated because
the electron cloud effect could be neglected as the LHC
was running with a bunch spacing of 50 ns. Results were
satisfactory in arc cells but extra heat loads were measured
TUPMB048 Proceedings of IPAC2016, Busan, Korea
ISBN 978-3-95450-147-2
1206
Copyright©2016CC-BY-3.0andbytherespectiveauthors
07 Accelerator Technology
T13 Cryogenics
Table 1: LHC Beam Screen Heat Load Constants
Name Description Value
L Beam screen length 53 m
¯E Nominal energy 7 TeV
Einj Injection energy 0.45 TeV
Eramp Final energy after ramp 6.5 TeV
¯Nb Nominal protons per bunch 1.15 · 1011
¯nb Nominal bunches per beam 2808
¯σ Nominal bunch length mean 1.06 ns
¯Qsr Nominal synch. rad. load 0.165 W/m
¯Qic Nominal image current load 0.135 W/m
p Bunch dependence factor −1.5
in Inner Triplets [7] due to the increase of the electron cloud
contribution, coming from the presence of the two counter
rotating beams in the same beam screen [8].
From 2015, all beam screen heat loads are directly cal-
culated and displayed online performing an energy balance
in the beam screen cooling loops [9]. The measured heat
loads were much higher than expected when the bunch spac-
ing was reduced to 25 ns and very important differences
between sectors were observed (1.4 W/m per aperture in
arc12 and 0.42 W/m in arc34). Nevertheless, the set of equa-
tions (2), (3) and (4) allows us to properly estimate these
heat loads thanks to the tuning parameters in the electron
cloud term. Beam screen heat loads and their estimations
are compared in Figure 4. Hence, this set of equations is
suitable to perform efficient feed-forward actions.
Figure 4: Measured and estimated deposited beam screen
heat loads in November 2015.
RESULTS OBTAINED IN 2015
During 2015 the improved control scheme embedding
the feed-forward compensations has been deployed pro-
gressively in the 16 Siemens PLCs (Programmable Logic
Controller) in charge of the LHC cryogenic control along
the ring. After the parameter tuning, the improved control
scheme yielded very good results where all temperatures
were correctly controlled at 20 K without overshoot. More-
over, the refrigeration power was optimized during transients
by quickly shutting-off all beam screen heaters during the
injection, see Figure 5.
Figure 5: Measurements in November 2015 during the fill #
4569 in 3 LHC sectors (2244 bunches at 6.5 TeV).
CONCLUSION
Since 2015 the LHC cryogenic system is significantly
disturbed by beam induced effects, mainly because of the
electron cloud effect when the bunch spacing is 25 ns. These
fast dynamic heat loads represent up to 25% of the installed
cryogenic refrigeration power and automatic compensation
is then mandatory in the cryogenic control system to cor-
rectly manage the transients. To handle these heat loads,
a feed-forward control based on the beam information has
been deployed on the 485 beam screen cooling loops along
the LHC, allowing to ramp the beam intensity up to 2244
bunches in 2015. For the rest of the LHC Run 2, the beam
screen heat loads estimation will be refined and the LHC re-
frigeration power should be ready for 1.5 W/m per aperture,
as compared to the 0.8 W/m initially forecast thanks to less
heat load on the 1.9 K cold mass than expected.
ACKNOWLEDGMENTS
This work has been possible only with the help of the
cryogenics operation team for the tuning and with the col-
laboration of the LHC beam operation team.
Proceedings of IPAC2016, Busan, Korea TUPMB048
07 Accelerator Technology
T13 Cryogenics
ISBN 978-3-95450-147-2
1207
Copyright©2016CC-BY-3.0andbytherespectiveauthors
REFERENCES
[1] V. Baglin, Ph. Lebrun, L. Tavian, R. van Weelderen, “Cryo-
genic Beam Screens for High-Energy Particle Accelerators”,
ICEC 24, Fukuoka, Japan (2012).
[2] G. Iadarola , G. Arduini, V. Baglin, H. Bartosik, J. Esteban
Muller, G. Rumolo, E. Shaposhnikova, L. Tavian, F. Zim-
mermann , O. Domínguez "Electron Cloud and Scrubbing
Studies for the LHC", IPAC13, Shanghai, China (2013).
[3] K. Li, H. Bartosik, G. Iadarola, L. Mether, A. Romano, G.
Rumolo, M. Schenk, "Electron cloud observations during
LHC operation with 25 ns beams", IPAC16, Busan, Korea
(2016).
[4] O. Bruning et al., “LHC Design Report”, CERN, Geneva
(2004).
[5] B. Bradu, E. Rogez, E. Blanco, G. Ferlin, A. Tovar, “ Beam
screen cryogenic control improvements for the LHC run 2”,
ICEC 26, New-Delhi, India (2016).
[6] B. Bradu, E. Blanco, P. Gayet, “Example of cryogenic process
simulation using EcosimPro: LHC beam screens cooling
circuits”, Cryogenics, 53:45-50 (2013).
[7] L. Tavian, “Performance limitations of the LHC cryogen-
ics: 2012 review and 2015 outlook”, LHC Beam Operation
workshop, Evian, France (2012).
[8] C. Zannini, G.Iadarola, G. Rumolo, "Power Loss Calculation
in Separated and Common Beam Chambers of the LHC",
IPAC14, Dresden, Germany (2014).
[9] K. Brodzinski, L. Tavian, “First Measurements of Beam-
Induced Heating on the LHC Cryogenic System”, ICEC 24,
Fukuoka, Japan (2012).
TUPMB048 Proceedings of IPAC2016, Busan, Korea
ISBN 978-3-95450-147-2
1208
Copyright©2016CC-BY-3.0andbytherespectiveauthors
07 Accelerator Technology
T13 Cryogenics

Weitere ähnliche Inhalte

Was ist angesagt?

practice problems on heat and thermodynamics
practice problems on heat and thermodynamicspractice problems on heat and thermodynamics
practice problems on heat and thermodynamicsShahjahan Physics
 
Lecture 3-4: Exergy, Heating and Cooling, Solar Thermal
Lecture 3-4: Exergy, Heating and Cooling, Solar ThermalLecture 3-4: Exergy, Heating and Cooling, Solar Thermal
Lecture 3-4: Exergy, Heating and Cooling, Solar Thermalcdtpv
 
J2006 termodinamik 1 unit9
J2006 termodinamik 1 unit9J2006 termodinamik 1 unit9
J2006 termodinamik 1 unit9Malaysia
 
2nd law of thermodynamics, entropy
2nd law of thermodynamics, entropy2nd law of thermodynamics, entropy
2nd law of thermodynamics, entropyposhiyabhavin
 
Experimental and Modeling Dynamic Study of the Indirect Solar Water Heater: A...
Experimental and Modeling Dynamic Study of the Indirect Solar Water Heater: A...Experimental and Modeling Dynamic Study of the Indirect Solar Water Heater: A...
Experimental and Modeling Dynamic Study of the Indirect Solar Water Heater: A...IJAAS Team
 
Solutions manual chapter 7 (1)
Solutions manual   chapter 7 (1)Solutions manual   chapter 7 (1)
Solutions manual chapter 7 (1)Omar Corral
 
Ch 15 Thermodynamics
Ch 15 ThermodynamicsCh 15 Thermodynamics
Ch 15 ThermodynamicsScott Thomas
 
Thermodynamics chapter 3
Thermodynamics chapter 3Thermodynamics chapter 3
Thermodynamics chapter 3zirui lau
 
Thermodynamic Chapter 3 First Law Of Thermodynamics
Thermodynamic Chapter 3 First Law Of ThermodynamicsThermodynamic Chapter 3 First Law Of Thermodynamics
Thermodynamic Chapter 3 First Law Of ThermodynamicsMuhammad Surahman
 
THERMODYNAMIC SYSTEMS
THERMODYNAMIC SYSTEMSTHERMODYNAMIC SYSTEMS
THERMODYNAMIC SYSTEMSselvakumar948
 
Work done in Isothermal and adiabatic Process
Work done in Isothermal and adiabatic ProcessWork done in Isothermal and adiabatic Process
Work done in Isothermal and adiabatic ProcessDeepanshu Chowdhary
 
Physics Thermodynamics
Physics ThermodynamicsPhysics Thermodynamics
Physics ThermodynamicsHaroun Elmir
 
02 part3 work heat transfer first law
02 part3 work heat transfer first law02 part3 work heat transfer first law
02 part3 work heat transfer first lawgunabalan sellan
 
SSL9 The Second Law of Thermodynamics
SSL9 The Second Law of ThermodynamicsSSL9 The Second Law of Thermodynamics
SSL9 The Second Law of ThermodynamicsKeith Vaugh
 
Ch 6a 2nd law
Ch 6a  2nd lawCh 6a  2nd law
Ch 6a 2nd lawabfisho
 

Was ist angesagt? (20)

Unit 1.2 thm
Unit 1.2 thmUnit 1.2 thm
Unit 1.2 thm
 
practice problems on heat and thermodynamics
practice problems on heat and thermodynamicspractice problems on heat and thermodynamics
practice problems on heat and thermodynamics
 
Lecture 3-4: Exergy, Heating and Cooling, Solar Thermal
Lecture 3-4: Exergy, Heating and Cooling, Solar ThermalLecture 3-4: Exergy, Heating and Cooling, Solar Thermal
Lecture 3-4: Exergy, Heating and Cooling, Solar Thermal
 
J2006 termodinamik 1 unit9
J2006 termodinamik 1 unit9J2006 termodinamik 1 unit9
J2006 termodinamik 1 unit9
 
2nd law of thermodynamics, entropy
2nd law of thermodynamics, entropy2nd law of thermodynamics, entropy
2nd law of thermodynamics, entropy
 
Experimental and Modeling Dynamic Study of the Indirect Solar Water Heater: A...
Experimental and Modeling Dynamic Study of the Indirect Solar Water Heater: A...Experimental and Modeling Dynamic Study of the Indirect Solar Water Heater: A...
Experimental and Modeling Dynamic Study of the Indirect Solar Water Heater: A...
 
Solutions manual chapter 7 (1)
Solutions manual   chapter 7 (1)Solutions manual   chapter 7 (1)
Solutions manual chapter 7 (1)
 
Ch 15 Thermodynamics
Ch 15 ThermodynamicsCh 15 Thermodynamics
Ch 15 Thermodynamics
 
Ch 6b 2nd law
Ch 6b  2nd lawCh 6b  2nd law
Ch 6b 2nd law
 
Thermodynamics chapter 3
Thermodynamics chapter 3Thermodynamics chapter 3
Thermodynamics chapter 3
 
Thermodynamic Chapter 3 First Law Of Thermodynamics
Thermodynamic Chapter 3 First Law Of ThermodynamicsThermodynamic Chapter 3 First Law Of Thermodynamics
Thermodynamic Chapter 3 First Law Of Thermodynamics
 
THERMODYNAMIC SYSTEMS
THERMODYNAMIC SYSTEMSTHERMODYNAMIC SYSTEMS
THERMODYNAMIC SYSTEMS
 
Work done in Isothermal and adiabatic Process
Work done in Isothermal and adiabatic ProcessWork done in Isothermal and adiabatic Process
Work done in Isothermal and adiabatic Process
 
Physics Thermodynamics
Physics ThermodynamicsPhysics Thermodynamics
Physics Thermodynamics
 
Ch04
Ch04Ch04
Ch04
 
Thermodynamics, part 3.ppt
Thermodynamics, part 3.pptThermodynamics, part 3.ppt
Thermodynamics, part 3.ppt
 
02 part3 work heat transfer first law
02 part3 work heat transfer first law02 part3 work heat transfer first law
02 part3 work heat transfer first law
 
Exergy
ExergyExergy
Exergy
 
SSL9 The Second Law of Thermodynamics
SSL9 The Second Law of ThermodynamicsSSL9 The Second Law of Thermodynamics
SSL9 The Second Law of Thermodynamics
 
Ch 6a 2nd law
Ch 6a  2nd lawCh 6a  2nd law
Ch 6a 2nd law
 

Ähnlich wie IPAC 2016_final

Impact of solar radiation and temperature levels on the variation of the seri...
Impact of solar radiation and temperature levels on the variation of the seri...Impact of solar radiation and temperature levels on the variation of the seri...
Impact of solar radiation and temperature levels on the variation of the seri...eSAT Journals
 
Cfdanalysisofaheatcollectorelementinasolarparabolictroughcollector 1401280320...
Cfdanalysisofaheatcollectorelementinasolarparabolictroughcollector 1401280320...Cfdanalysisofaheatcollectorelementinasolarparabolictroughcollector 1401280320...
Cfdanalysisofaheatcollectorelementinasolarparabolictroughcollector 1401280320...luthfiyyahadelia
 
Elk 26-6-32-1711-330
Elk 26-6-32-1711-330Elk 26-6-32-1711-330
Elk 26-6-32-1711-330Mellah Hacene
 
Feasibility evaluation of two solar cooling systems applied to a cuban
Feasibility evaluation of two solar cooling systems applied to a cubanFeasibility evaluation of two solar cooling systems applied to a cuban
Feasibility evaluation of two solar cooling systems applied to a cubanyamile diaz torres
 
Dynamic Simulation of a Hybrid Solar and Ocean Thermal Energy Conversion System
Dynamic Simulation of a Hybrid Solar and Ocean Thermal Energy Conversion SystemDynamic Simulation of a Hybrid Solar and Ocean Thermal Energy Conversion System
Dynamic Simulation of a Hybrid Solar and Ocean Thermal Energy Conversion SystemIJRES Journal
 
06 6377 9057-1-pb
06 6377 9057-1-pb06 6377 9057-1-pb
06 6377 9057-1-pbIAESIJEECS
 
Math cad effective radiation heat transfer coefficient.xmcd
Math cad   effective radiation heat transfer coefficient.xmcdMath cad   effective radiation heat transfer coefficient.xmcd
Math cad effective radiation heat transfer coefficient.xmcdJulio Banks
 
CFD Analysis of a Heat Collector Element in a Solar Parabolic Trough Collector
CFD Analysis of a Heat Collector Element in a Solar Parabolic Trough Collector CFD Analysis of a Heat Collector Element in a Solar Parabolic Trough Collector
CFD Analysis of a Heat Collector Element in a Solar Parabolic Trough Collector iMentor Education
 
Introduction to Magnetic Refrigeration
Introduction to Magnetic RefrigerationIntroduction to Magnetic Refrigeration
Introduction to Magnetic RefrigerationSamet Baykul
 
Cascade forward neural network based on resilient backpropagation for simulta...
Cascade forward neural network based on resilient backpropagation for simulta...Cascade forward neural network based on resilient backpropagation for simulta...
Cascade forward neural network based on resilient backpropagation for simulta...Mellah Hacene
 
Novel technique for maximizing the thermal efficiency of a hybrid pv
Novel technique for maximizing the thermal efficiency of a hybrid pvNovel technique for maximizing the thermal efficiency of a hybrid pv
Novel technique for maximizing the thermal efficiency of a hybrid pveSAT Journals
 
Effect analysis of the different channel length and depth of photovoltaic the...
Effect analysis of the different channel length and depth of photovoltaic the...Effect analysis of the different channel length and depth of photovoltaic the...
Effect analysis of the different channel length and depth of photovoltaic the...IJECEIAES
 
chap3firstlawthermodynamics-130703012634-phpapp02.ppt
chap3firstlawthermodynamics-130703012634-phpapp02.pptchap3firstlawthermodynamics-130703012634-phpapp02.ppt
chap3firstlawthermodynamics-130703012634-phpapp02.pptHIEUNGUYENHUU13
 
Design of a self tuning regulator for temperature control of a polymerization...
Design of a self tuning regulator for temperature control of a polymerization...Design of a self tuning regulator for temperature control of a polymerization...
Design of a self tuning regulator for temperature control of a polymerization...ISA Interchange
 
First Law of Thermodynamics: Energy Conservation For Mechanical and Industria...
First Law of Thermodynamics: Energy Conservation For Mechanical and Industria...First Law of Thermodynamics: Energy Conservation For Mechanical and Industria...
First Law of Thermodynamics: Energy Conservation For Mechanical and Industria...Kum Visal
 
HEAT TRANSFER
HEAT TRANSFER HEAT TRANSFER
HEAT TRANSFER oday hatem
 

Ähnlich wie IPAC 2016_final (20)

CERN-ACC-2016-0106
CERN-ACC-2016-0106CERN-ACC-2016-0106
CERN-ACC-2016-0106
 
GSA TUNED HIGH EXERGY IN PV ARRAY
GSA TUNED HIGH EXERGY IN PV ARRAYGSA TUNED HIGH EXERGY IN PV ARRAY
GSA TUNED HIGH EXERGY IN PV ARRAY
 
Impact of solar radiation and temperature levels on the variation of the seri...
Impact of solar radiation and temperature levels on the variation of the seri...Impact of solar radiation and temperature levels on the variation of the seri...
Impact of solar radiation and temperature levels on the variation of the seri...
 
Cfdanalysisofaheatcollectorelementinasolarparabolictroughcollector 1401280320...
Cfdanalysisofaheatcollectorelementinasolarparabolictroughcollector 1401280320...Cfdanalysisofaheatcollectorelementinasolarparabolictroughcollector 1401280320...
Cfdanalysisofaheatcollectorelementinasolarparabolictroughcollector 1401280320...
 
Elk 26-6-32-1711-330
Elk 26-6-32-1711-330Elk 26-6-32-1711-330
Elk 26-6-32-1711-330
 
Feasibility evaluation of two solar cooling systems applied to a cuban
Feasibility evaluation of two solar cooling systems applied to a cubanFeasibility evaluation of two solar cooling systems applied to a cuban
Feasibility evaluation of two solar cooling systems applied to a cuban
 
Dynamic Simulation of a Hybrid Solar and Ocean Thermal Energy Conversion System
Dynamic Simulation of a Hybrid Solar and Ocean Thermal Energy Conversion SystemDynamic Simulation of a Hybrid Solar and Ocean Thermal Energy Conversion System
Dynamic Simulation of a Hybrid Solar and Ocean Thermal Energy Conversion System
 
06 6377 9057-1-pb
06 6377 9057-1-pb06 6377 9057-1-pb
06 6377 9057-1-pb
 
Calculation method to estimate the sunlight intensity falling on flat panel s...
Calculation method to estimate the sunlight intensity falling on flat panel s...Calculation method to estimate the sunlight intensity falling on flat panel s...
Calculation method to estimate the sunlight intensity falling on flat panel s...
 
Math cad effective radiation heat transfer coefficient.xmcd
Math cad   effective radiation heat transfer coefficient.xmcdMath cad   effective radiation heat transfer coefficient.xmcd
Math cad effective radiation heat transfer coefficient.xmcd
 
J010337176
J010337176J010337176
J010337176
 
CFD Analysis of a Heat Collector Element in a Solar Parabolic Trough Collector
CFD Analysis of a Heat Collector Element in a Solar Parabolic Trough Collector CFD Analysis of a Heat Collector Element in a Solar Parabolic Trough Collector
CFD Analysis of a Heat Collector Element in a Solar Parabolic Trough Collector
 
Introduction to Magnetic Refrigeration
Introduction to Magnetic RefrigerationIntroduction to Magnetic Refrigeration
Introduction to Magnetic Refrigeration
 
Cascade forward neural network based on resilient backpropagation for simulta...
Cascade forward neural network based on resilient backpropagation for simulta...Cascade forward neural network based on resilient backpropagation for simulta...
Cascade forward neural network based on resilient backpropagation for simulta...
 
Novel technique for maximizing the thermal efficiency of a hybrid pv
Novel technique for maximizing the thermal efficiency of a hybrid pvNovel technique for maximizing the thermal efficiency of a hybrid pv
Novel technique for maximizing the thermal efficiency of a hybrid pv
 
Effect analysis of the different channel length and depth of photovoltaic the...
Effect analysis of the different channel length and depth of photovoltaic the...Effect analysis of the different channel length and depth of photovoltaic the...
Effect analysis of the different channel length and depth of photovoltaic the...
 
chap3firstlawthermodynamics-130703012634-phpapp02.ppt
chap3firstlawthermodynamics-130703012634-phpapp02.pptchap3firstlawthermodynamics-130703012634-phpapp02.ppt
chap3firstlawthermodynamics-130703012634-phpapp02.ppt
 
Design of a self tuning regulator for temperature control of a polymerization...
Design of a self tuning regulator for temperature control of a polymerization...Design of a self tuning regulator for temperature control of a polymerization...
Design of a self tuning regulator for temperature control of a polymerization...
 
First Law of Thermodynamics: Energy Conservation For Mechanical and Industria...
First Law of Thermodynamics: Energy Conservation For Mechanical and Industria...First Law of Thermodynamics: Energy Conservation For Mechanical and Industria...
First Law of Thermodynamics: Energy Conservation For Mechanical and Industria...
 
HEAT TRANSFER
HEAT TRANSFER HEAT TRANSFER
HEAT TRANSFER
 

IPAC 2016_final

  • 1. COMPENSATION OF BEAM INDUCED EFFECTS IN LHC CRYOGENIC SYSTEMS B. Bradu, E. Rogez, G. Iadarola, E. Blanco Viñuela, G. Ferlin, A. Tovar González, B. Fernández Adiego, P. Plutecki, CERN, Geneva, Switzerland Abstract This paper presents the different control strategies de- ployed in the LHC cryogenic system in order to compensate the beam induced effects in real-time. The LHC beam is inducing important heat loads along the 27 km of beam screens due to synchrotron radiation, image current and elec- tron clouds. These dynamic heat loads significantly disturb the cryogenic plants and automatic compensation is manda- tory to operate the LHC at full energy. The LHC beam screens must be maintained in an acceptable temperature range around 20 K to ensure a good beam vacuum, espe- cially during beam injections and energy ramping where the dynamic responses of cryogenic systems cannot be man- aged with conventional feedback control techniques. Con- sequently, several control strategies such as feed-forward compensation have been developed and deployed success- fully on the machine during 2015 where the beam induced heat loads are forecast in real-time to anticipate their future effects on cryogenic systems. All these developments have been first entirely modelled and simulated dynamically in order to be validated, allowing then a smooth deployment during the LHC operation. INTRODUCTION In most superconducting particle accelerators such as the LHC (Large Hadron Collider) at CERN (European Organisa- tion for Nuclear Research), the dedicated cryogenic systems are used to maintain the superconductivity in the magnets and RF cavities. Nevertheless, particle beams travelling through the magnets can generate important heat losses in the beam vacuum chambers and these dynamic heat loads must be removed as well by the cryogenic systems. For this reason, the LHC embeds a beam screen within the beam pipe in order to remove the beam induced effects and to ensure an ultra-high vacuum [1]. LHC BEAM SCREENS The LHC beam screens are cooled by supercritical helium at 3 bar and 4.6 K provided by cryogenic refrigerators located at the surface. Helium is then heated up to 20 K by the beam induced effects or by an electrical heater when the beam is not present. The beam screen temperature has to be maintained as stable as possible to ensure an ultra high vacuum in the beam pipe and a stable gaseous helium return flow to the refrigerators. The temperature is controlled by a heater at the inlet and by a control valve at the outlet, see Figure 1. In total, the LHC contains 485 beam screen cooling loops over the 27 km and a typical loop in the arc measures 53 m (half-cell). Figure 1: LHC beam screen cooling scheme. During Run 1 of the LHC in 2010-2012, the deposited beam screen heat loads (Qdbs) were pretty low, about 0.1 W/m per aperture, corresponding to only 1.7 % of the total LHC refrigeration capacity at 4.5 K. As particle bunches were separated by a gap of 50 ns only, these heat loads were induced by synchrotron radiations and image current [2]. For LHC Run 2 (2015-2018), the bunch spacing was reduced to 25 ns in order to get higher beam inten- sity and thus electron cloud effects become dominant in the beam screen heat loads [3]. The LHC design report fore- casts a total beam screen heat load of about 0.8 W/m per aperture at nominal conditions [4] which represents a signif- icant part of the installed cryogenic capacity at 4.5 K (about 12 %). As these heat loads are dynamic and can evolve rapidly compared to the cryogenic system inertia, automatic compensation is mandatory in the cryogenic control system. BEAM SCREENS TEMPERATURE CONTROL The beam screen inlet temperature is maintained above a minimum temperature using the electrical heater at the entrance of the circuit to avoid thermo-hydraulic oscilla- tions and the outlet temperature is controlled at 20 K using the control valve. To perform these controls, classical PID (Proportional-Integral-Derivative) controllers are used. Un- fortunately, the time constants and delays of the cryogenic systems regarding the beam induced heat load dynamics are not suitable for such feedback loops and the PID controllers cannot reject these disturbances efficiently [5]. To improve the control of the temperature ensuring its stability, feed-forward actions on the valve and on the heater have been added to prevent the beam disturbances. These feed-forward contributions are calculated from the available beam information (energy, intensities, number of bunches Proceedings of IPAC2016, Busan, Korea TUPMB048 07 Accelerator Technology T13 Cryogenics ISBN 978-3-95450-147-2 1205 Copyright©2016CC-BY-3.0andbytherespectiveauthors
  • 2. and bunch length means) to estimate the beam induced heat loads and to position the valve and the heater close to their fi- nal expected values, see Figure 2 where the control scheme is represented, embedding the feed-forward transfer functions FF1 and FF2. Moreover, the inlet temperature set-point is switched between 13 K and 6 K according to the presence of the beam to save energy. Figure 2: LHC beam screen control scheme. This control scheme has been validated using dynamic simulations performed with the modeling and simulation software EcosimPro and the cryogenic library CRYOLIB [6], see Figure 3, where two dynamic simulations are compared. Once validated in simulation, this improved control scheme has been successfully deployed in 2015 throughout the en- tire LHC cryogenic control system. Nevertheless, to have these feed-forward actions working in an efficient way, it is necessary to correctly estimate the deposited heat load on the beam screens in real-time, otherwise a temperature over- shoot or a sub-cooling of the beam screens can be observed. Figure 3: Simulations of the beam screen cryogenic circuit during a beam injection generating 1.2 W/m per aperture with and without Feed-Forward actions (FF). DEPOSITED HEAT LOAD ESTIMATIONS IN BEAM SCREENS Computation of beam screen heat loads The deposited heat load on the beam screens Qdbs can be computed as the sum of three main induced heat loads contributions as represented in equation (1): synchrotron radiations Qsr , image current Qic and electron clouds Qec. Qdbs = Qsr + Qic + Qec (1) Synchrotron radiation and image current contributions can be estimated by scaling their design values with the beam parameters [7] as represented in equations (2) and (3). The Table 1 describes all constants. Qsr = ¯Qsr · L · E ¯E 4 · Nb ¯Nb · nb ¯nb (2) Qic = ¯Qic ·L· 0.6 · E + 2800 ¯E · Nb ¯Nb 2 · nb ¯nb · σ ¯σ p (3) Concerning the electron cloud contribution Qec, there is no such simple scaling as it depends on the injection scheme of the machine (i.e. including the number and length of the gaps that are present in the bunch train) and on the condition- ing of the surface where electrons are generated. Moreover, this term has a tendency to decrease with the running time of the machine as the beam circulation gradually conditions the surface. Nevertheless, the electron cloud heat load increases with the intensity and the energy of the beam. It is then possible to establish from measurements along the ring an electron cloud heat load value per bunch for each part of the machine at the injection energy (qeci) and at the final energy after the ramp (qecr ). Finally, the equation (4) was used to model the electron cloud contribution for each LHC sector. The terms qeci and qecr are the measured electron cloud heat load per bunch at the beginning of the run. The terms Kecr and Keci are tuning parameters to adjust the heat load estimations according to the conditioning taking place in the machine along the run: these two terms are equal to 1.0 at the be- ginning of the run and they are then decreased manually by operators according to the cleaning effect. Qec = Keci · qeci 2 · 1 − E−Ein j Er amp −Ein j · nb · Nb ¯Nb +Kecr · qecr 2 · E−Ein j Er amp −Ein j · nb · Nb ¯Nb (4) Comparisons with real measurements During Run 1, the synchrotron radiation and the image current contributions could easily be calculated because the electron cloud effect could be neglected as the LHC was running with a bunch spacing of 50 ns. Results were satisfactory in arc cells but extra heat loads were measured TUPMB048 Proceedings of IPAC2016, Busan, Korea ISBN 978-3-95450-147-2 1206 Copyright©2016CC-BY-3.0andbytherespectiveauthors 07 Accelerator Technology T13 Cryogenics
  • 3. Table 1: LHC Beam Screen Heat Load Constants Name Description Value L Beam screen length 53 m ¯E Nominal energy 7 TeV Einj Injection energy 0.45 TeV Eramp Final energy after ramp 6.5 TeV ¯Nb Nominal protons per bunch 1.15 · 1011 ¯nb Nominal bunches per beam 2808 ¯σ Nominal bunch length mean 1.06 ns ¯Qsr Nominal synch. rad. load 0.165 W/m ¯Qic Nominal image current load 0.135 W/m p Bunch dependence factor −1.5 in Inner Triplets [7] due to the increase of the electron cloud contribution, coming from the presence of the two counter rotating beams in the same beam screen [8]. From 2015, all beam screen heat loads are directly cal- culated and displayed online performing an energy balance in the beam screen cooling loops [9]. The measured heat loads were much higher than expected when the bunch spac- ing was reduced to 25 ns and very important differences between sectors were observed (1.4 W/m per aperture in arc12 and 0.42 W/m in arc34). Nevertheless, the set of equa- tions (2), (3) and (4) allows us to properly estimate these heat loads thanks to the tuning parameters in the electron cloud term. Beam screen heat loads and their estimations are compared in Figure 4. Hence, this set of equations is suitable to perform efficient feed-forward actions. Figure 4: Measured and estimated deposited beam screen heat loads in November 2015. RESULTS OBTAINED IN 2015 During 2015 the improved control scheme embedding the feed-forward compensations has been deployed pro- gressively in the 16 Siemens PLCs (Programmable Logic Controller) in charge of the LHC cryogenic control along the ring. After the parameter tuning, the improved control scheme yielded very good results where all temperatures were correctly controlled at 20 K without overshoot. More- over, the refrigeration power was optimized during transients by quickly shutting-off all beam screen heaters during the injection, see Figure 5. Figure 5: Measurements in November 2015 during the fill # 4569 in 3 LHC sectors (2244 bunches at 6.5 TeV). CONCLUSION Since 2015 the LHC cryogenic system is significantly disturbed by beam induced effects, mainly because of the electron cloud effect when the bunch spacing is 25 ns. These fast dynamic heat loads represent up to 25% of the installed cryogenic refrigeration power and automatic compensation is then mandatory in the cryogenic control system to cor- rectly manage the transients. To handle these heat loads, a feed-forward control based on the beam information has been deployed on the 485 beam screen cooling loops along the LHC, allowing to ramp the beam intensity up to 2244 bunches in 2015. For the rest of the LHC Run 2, the beam screen heat loads estimation will be refined and the LHC re- frigeration power should be ready for 1.5 W/m per aperture, as compared to the 0.8 W/m initially forecast thanks to less heat load on the 1.9 K cold mass than expected. ACKNOWLEDGMENTS This work has been possible only with the help of the cryogenics operation team for the tuning and with the col- laboration of the LHC beam operation team. Proceedings of IPAC2016, Busan, Korea TUPMB048 07 Accelerator Technology T13 Cryogenics ISBN 978-3-95450-147-2 1207 Copyright©2016CC-BY-3.0andbytherespectiveauthors
  • 4. REFERENCES [1] V. Baglin, Ph. Lebrun, L. Tavian, R. van Weelderen, “Cryo- genic Beam Screens for High-Energy Particle Accelerators”, ICEC 24, Fukuoka, Japan (2012). [2] G. Iadarola , G. Arduini, V. Baglin, H. Bartosik, J. Esteban Muller, G. Rumolo, E. Shaposhnikova, L. Tavian, F. Zim- mermann , O. Domínguez "Electron Cloud and Scrubbing Studies for the LHC", IPAC13, Shanghai, China (2013). [3] K. Li, H. Bartosik, G. Iadarola, L. Mether, A. Romano, G. Rumolo, M. Schenk, "Electron cloud observations during LHC operation with 25 ns beams", IPAC16, Busan, Korea (2016). [4] O. Bruning et al., “LHC Design Report”, CERN, Geneva (2004). [5] B. Bradu, E. Rogez, E. Blanco, G. Ferlin, A. Tovar, “ Beam screen cryogenic control improvements for the LHC run 2”, ICEC 26, New-Delhi, India (2016). [6] B. Bradu, E. Blanco, P. Gayet, “Example of cryogenic process simulation using EcosimPro: LHC beam screens cooling circuits”, Cryogenics, 53:45-50 (2013). [7] L. Tavian, “Performance limitations of the LHC cryogen- ics: 2012 review and 2015 outlook”, LHC Beam Operation workshop, Evian, France (2012). [8] C. Zannini, G.Iadarola, G. Rumolo, "Power Loss Calculation in Separated and Common Beam Chambers of the LHC", IPAC14, Dresden, Germany (2014). [9] K. Brodzinski, L. Tavian, “First Measurements of Beam- Induced Heating on the LHC Cryogenic System”, ICEC 24, Fukuoka, Japan (2012). TUPMB048 Proceedings of IPAC2016, Busan, Korea ISBN 978-3-95450-147-2 1208 Copyright©2016CC-BY-3.0andbytherespectiveauthors 07 Accelerator Technology T13 Cryogenics