Heat load analysis for Inner Triplet and Stand Alone Modules H. Bartosik, J. Hulsmann, G. Iadarola and G. Rumolo LBOC meeting 28 October 2014 Based on heat load data and tools by: S. Claudet, S. Popescu, L. Tavian, J. Wenninger Many thanks to: G. Arduini, E. Metral C. Zannini

Outline Introduction Run 1 heat load data analysis o Inner Triplets o Matching Quadrupoles o D3L4 Stand Alone dipole SEY reconstruction through PyECLOUD simulations

Outline Introduction Run 1 heat load data analysis o Inner Triplets o Matching Quadrupoles o D3L4 Stand Alone dipole SEY reconstruction through PyECLOUD simulations

Heat load measurements The measurement of the heat load on the beam screen of the cold elements of the LHC proved to be a fundamental tool for monitoring and studying electron cloud effects  For example, comparing these data against PyECLOUD simulations we could reconstruct the evolution of the SEY in the arc dipoles during the 25 ns tests Tests with 25 ns beams in 2011

Heat load measurements Originally heat loads were computed off line by the cryogenics team based on data available in the logging database The 2011 experience showed that it would had been extremely useful to have the heat load information available in the CCC during scrubbing runs in order to optimize and steer the scrubbing process. During the 2012 scrubbing the TE-CRG team provided us with an excel tool to compute the heat loads in two of the arcs  proved to be very effective to follow the scrubbing process

Heat load measurements During LS1 there was a significant effort by TE-CRG and BE-OP to develop an “operational” tool for “real- time” heat load computation Heat loads were implemented in the LHC logging database as virtual variables i.e. computed on request from other stored data The different cooling circuits became gradually available during 2014  at the moment the LHC is practically fully covered Data from Run 1 are also available in the database See also: S. Popescu, Cryogenic heat load information for operation, LBOC meeting 6 May 2014

Outline Introduction Run 1 heat load data analysis o Inner Triplets o Matching Quadrupoles o D3L4 Stand Alone dipole SEY reconstruction through PyECLOUD simulations

Run 1 data analysis We decided to start using this tool to analyze Run 1 data: We could use the command line interface to the database to perform systematic (fill by fill) data extraction to study long term behavior (practically impossible with the “manual” data extraction) The exercise was very useful for us to develop analysis tools in view of the 2015 scrubbing runs and for testing and debugging the new system Extremely valuable help from Johannes Hulsmann, who worked with us as a Summer Student on this topic We decided to focus on the Inner Triplets since these were the only devices where a strong heat load due to electron cloud could be observed during operation with 50 ns in Run 1 (due to the presence of the two beams in the same chamber) Stand Alone Magnets (SAMs) with separated chambers were also considered for comparison

Outline Introduction Run 1 heat load data analysis o Inner Triplets o Matching Quadrupoles o D3L4 Stand Alone dipole SEY reconstruction through PyECLOUD simulations

Strong heat load due to electron cloud observed only with two circulating beams Heat load in the Inner Triplets with 50 ns: basic features

No big change during ramp and squeeze Heat load in the Inner Triplets with 50 ns: basic features

No big change during ramp and squeeze Heat load in the Inner Triplets with 50 ns: basic features

Quite big spread between different devices (especially in 2011 ??) Offset error (we correct it using value measured in Injection Probe Beam mode) Strong oscillations (also without beam)  source to be identified Heat load in the Inner Triplets with 50 ns: basic features

To study the evolution of the heat load during Run 1, we extracted for each fill the average heat load measured during the first 30 min in stable beams (offset error corrected using InjProbe)  Strong correlation with beam intensity Inner Triplets: evolution during Run p-p Run2012 p-p Run (starting from 1 March 2011)

Intensity threshold To study the evolution of the heat load during Run 1, we extracted for each fill the average heat load measured during the first 30 min in stable beams (offset error corrected using InjProbe)  Strong correlation with beam intensity (practically linear dependence) Inner Triplets: evolution during Run 1

Normalizing the heat load to the beam intensity, we cannot recognize strong signs of conditioning  Faint sign of scrubbing effect is visible at the beginning of 2011 and 2012 Inner Triplets: evolution during Run 1 (starting from 1 March 2011) 2011 p-p Run2012 p-p Run Scrubbing with 50 ns Scrubbing with 25 ns

Heat loads measured during the Scrubbing Run with 50 ns beams in April 2011 quite similar to what was observed in physics fills Inner Triplets: scrubbing with 50 ns (April 2011) 50 ns physics

Heat loads measured during the Scrubbing Run with 25 ns is stronger compared to the 50 ns cases (same total intensity) Inner Triplets: scrubbing with 25 ns (December 2012) 50 ns physics

Outline Introduction Run 1 heat load data analysis o Inner Triplets o Matching Quadrupoles o D3L4 Stand Alone dipole SEY reconstruction through PyECLOUD simulations

To study the evolution of the heat load during Run 1, we extracted for each fill the average heat load measured during the first 30 min in stable beams (offset error corrected using InjProbe)  Quite low values, compatible with beam screen impedance heating 2011 p-p Run2012 p-p Run (starting from 1 March 2011) Q5 and Q6 matching quads: evolution during Run 1

To study the evolution of the heat load during Run 1, we extracted for each fill the average heat load measured during the first 30 min in stable beams (offset error corrected using InjProbe)  Quite low values, compatible with beam screen impedance heating Q5 and Q6 matching quads: evolution during Run 1 Estimated impedance heating (courtesy C. Zannini)

Heat loads measured during the Scrubbing Run with 25 ns is much stronger compared to the 50 ns cases (same total intensity)  ecloud developing only with 25 ns beams Q5 and Q6 quads: : scrubbing with 25 ns (December 2012) 50 ns physics

Outline Introduction Run 1 heat load data analysis o Inner Triplets o Matching Quadrupoles o D3L4 Stand Alone dipole SEY reconstruction through PyECLOUD simulations

To study the evolution of the heat load during Run 1, we extracted for each fill the average heat load measured during the first 30 min in stable beams (offset error corrected using InjProbe)  Quite low values, compatible with beam screen impedance heating 2011 p-p Run2012 p-p Run (starting from 1 March 2011) D3L4 stand alone dipole: evolution during Run 1

To study the evolution of the heat load during Run 1, we extracted for each fill the average heat load measured during the first 30 min in stable beams (offset error corrected using InjProbe)  Quite low values, compatible with beam screen impedance heating D3L4 stand alone dipole: evolution during Run 1

Heat loads measured during the Scrubbing Run with 25 ns is much stronger compared to the 50 ns cases (same total intensity)  ecloud developing with 25 ns beams D3L4 stand alone dipole: evolution during Run 1 50 ns physics

Outline Introduction Run 1 heat load data analysis o Inner Triplets o Matching Quadrupoles o D3L4 Stand Alone dipole SEY reconstruction through PyECLOUD simulations

2011 p-p Run2012 p-p Run (starting from 1 March 2011) To have a reliable estimation both for the Inner Triplets and for the Matching Quadrupoles, we decided to use measurements taken with 25 ns beams  Two fills with similar intensity and filling pattern (fills 2251 in 2011 and 3438 in 2012) Fill 2251Fill 3438 SEY reconstruction through PyECLOUD simulations

To have a reliable estimation both for the Inner Triplets and for the Matching Quadrupoles, we decided to use measurements taken with 25 ns beams  Two fills with similar intensity and filling pattern (fills 2251 in 2011 and 3438 in 2012) Innner triplets SEY reconstruction through PyECLOUD simulations

To have a reliable estimation both for the Inner Triplets and for the Matching Quadrupoles, we decided to use measurements taken with 25 ns beams  Two fills with similar intensity and filling pattern (fills 2251 in 2011 and 3438 in 2012) Q5 and Q6 IR1 and 5 SEY reconstruction through PyECLOUD simulations

Fill 2251 (2011) Fill 3438 (2012) Simulations were setup with measured bunch intensities and bunch lengths

Different PyECLOUD simulations had to be run for different longitudinal positions along the triplets in order to account for the different beam positions, beam size and for the different delays between the two beams Inferred values are very low: 1.1<SEY<1.2 and seem to confirm a slight conditioning Measured SEY reconstruction through PyECLOUD simulations

Measured Results very similar to the triplets: Inferred values are very low: 1.1<SEY<1.2 and seem to indicate a slight conditioning SEY reconstruction through PyECLOUD simulations Simulations performed for Q5R1

Concluding remarks Heat load calculations fro the LHC beam screens have been implemented as virtual variables in the LHC Logging Database (by BE-OP and TE-CRG) This has allowed us to perform a systematic analysis of the heat load behavior in the Inner Triples and Stand Alone Modules (SAMs) during Run 1 During the physics with 50 ns beams: The heat load in the triplets exhibits a linear dependence on the total beam intensity Heat load in the SAMs was found to compatible with impedance heating Comparing the data against PyECLOUD simulations a value of SEY between 1.1 and 1.2 was inferred for both the inner triplets and matching quadrupoles The natural next step would be to apply the same procedure to the LHC arcs (also for 2015 data) Huge amount of data (many cooling circuits, one per half cell)  it would be very useful to have the time averaging/undersampling functions of the logging data extraction available also on these data It would be desirable to have total heat load per arc directly available in the database (also for online monitoring during 2015 Scrubbing Runs)

Thanks for your attention!

Q5

Matching section IP5 - Present LHC a b BS1 a=22.5, b=17.6 BS2 a=28.9, b=24 BSArc a=22, b=17.15 BSD2 a=31.3, b=26.4 Beam screens can be rotated Q4 BS2 Q5 BS1 Q7 BSArc Q6 BS1 D2 - BSD2 Beam off center

Matching section IP2 – Present LHC a b Q4 BS2 Q5 BS1 Q7 BSArc Q6 BS1 D2 - BSD2 Q4 BS2 Q5 BS2 Q7 BSArc Q6 BS1 D2 - BSD2 BS1 a=22.5, b=17.6 BS2 a=28.9, b=24 BSArc a=22, b=17.15 BSD2 a=31.3, b=26.4 Beam screens can be rotated Inj. beam