Optimum design of hydrodynamic thrust bearings with rayleigh's pocket profiles

Optimum design problem for hydrodynamic self-aligning acting thrust bearings was considered. Based on results for rectangular region the problem for sector region was solved. As an objective function, the maximum of pressure integral over the lubricant layer surface was used and five geometrical parameters described Rayleigh's pocket shape were used as optimization variables during optimization procedure. The bearing pressure distribution was determined on the basis of the Navier-Stokes equations using the ANSYS / CFX software. Numerically the optimization problem was solved using three different methods: IOSO, SIMPLEX and pilOPT+AFilter SQP realized in two commercial optimization software IOSO and modeFRONTIER. The aim of this investigation was designing the technologically advanced profiles of thrust bearing sector microgeometry ensuring the maximum load capacity

Optimum design of hydrodynamic thrust bearings with rayleigh's pocket profiles

Optimum design problem for hydrodynamic self-aligning acting thrust bearings was considered. Based on results for rectangular region the problem for sector region was solved. As an objective function, the maximum of pressure integral over the lubricant layer surface was used and five geometrical parameters described Rayleigh's pocket shape were used as optimization variables during optimization procedure. The bearing pressure distribution was determined on the basis of the Navier-Stokes equations using the ANSYS / CFX software. Numerically the optimization problem was solved using three different methods: IOSO, SIMPLEX and pilOPT+AFilter SQP realized in two commercial optimization software IOSO and modeFRONTIER. The aim of this investigation was designing the technologically advanced profiles of thrust bearing sector microgeometry ensuring the maximum load capacity

Deep Learning Approaches for LHCb ECAL Reconstruction

Calorimeters are a crucial component for most detectors mounted on modern colliders. Their tasks include identifying and measuring the energy of photons and neutral hadrons, recording energetic hadronic jets, and contributing to the identification of electrons, muons, and charged hadrons. To fulfill these many tasks while keeping costs reasonable, the calorimeter construction requires good and thoughtful balancing with other components of the detector. Much harder operation conditions during LHC’s high luminosity Run 5 and beyond bring new technological and computational challenges. This requires optimization of technologies, layouts, readouts, reconstruction algorithms to achieve the best overall physics performance for the limited cost. In the traditional approach, the reconstruction of the physical objects in the calorimeter must be matched to the calorimetric showers simulation used. We present a deep learning-based approach to help utilize raw simulated calorimetric data of varying degrees of detail

Advanced Numerical Models for Design and Optimization of Thrust Bearing Hydrodynamic Characteristics

Efficiency of hydrodynamic thrust bearings used in a wide range of power machines is characterized by including the load capacity of an oil wedge, which has nonlinear dependence on gap size. In this study we consider the different types of lubricant layer microgeometry profiling with the aim of optimal design of the hydrodynamic bearing characteristics for ensuring the maximum load capacity using advanced numerical models and methods. We enlarge the results of significant research works of J.W. Rayleigh and S. Y. Maday in relation to the hydrodynamic sector self-aligning acting thrust bearings based on advanced numerical methods. Different geometrical parameters which define profile curvature were used as optimization variables. The maximum of pressure integral over the lubricant layer surface as objective function was used. Hydrodynamic problems using Navier-Stocks equations were solved based on numerical approach and commercial CFD code ANSYS/CFX using the St.Petersburg Polytechnic Supercomputer Center

Reconstruction of primary vertices at the ATLAS experiment in Run 1 proton–proton collisions at the LHC

This paper presents the method and performance of primary vertex reconstruction in proton–proton collision data recorded by the ATLAS experiment during Run 1 of the LHC. The studies presented focus on data taken during 2012 at a centre-of-mass energy of √s=8 TeV. The performance has been measured as a function of the number of interactions per bunch crossing over a wide range, from one to seventy. The measurement of the position and size of the luminous region and its use as a constraint to improve the primary vertex resolution are discussed. A longitudinal vertex position resolution of about 30μm is achieved for events with high multiplicity of reconstructed tracks. The transverse position resolution is better than 20μm and is dominated by the precision on the size of the luminous region. An analytical model is proposed to describe the primary vertex reconstruction efficiency as a function of the number of interactions per bunch crossing and of the longitudinal size of the luminous region. Agreement between the data and the predictions of this model is better than 3% up to seventy interactions per bunch crossing

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Towards spontaneous parametric down-conversion at low temperatures

The possibility of observing spontaneous parametric down-conversion in doped nonlinear crystals at low temperatures, which would be useful for combining heralded single-photon sources and quantum memories, is studied theoretically. The ordinary refractive index of a lithium niobate crystal doped with magnesium oxide LiNbO3:MgO is measured at liquid nitrogen and helium temperatures. On the basis of the experimental data, the coefficients of the Sellmeier equation are determined for the temperatures from 5 to 300 K. In addition, a poling period of the nonlinear crystal has been calculated for observing type-0 spontaneous parametric down-conversion (ooo-synchronism) at the liquid helium temperature under pumping at the wavelength of λp = 532 nm and emission of the signal field at the wavelength of λs = 794 nm, which corresponds to the resonant absorption line of Tm3+ doped ions

Progress in end-to-end optimization of fundamental physics experimental apparata with differentiable programming

In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models we consider rely on differentiable programming methods and on the specification of a software pipeline including all factors impacting performance — from the data-generating processes to their reconstruction and the inference on the parameters of interest — along with the careful specification of a utility function well aligned with the end goals of the experiment. Building on previous studies originated within the MODE Collaboration, we focus specifically on applications involving instruments for particle physics experimentation, as well as industrial and medical applications that share the detection of radiation as their data-generating mechanism. This report illustrates the most recent advancements in the area, and outlines, for each of the discussed applications as well as for automatic differentiation itself, ongoing and future work.</p