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