ترغب بنشر مسار تعليمي؟ اضغط هنا

Additively manufactured ultra-high vacuum chamber below $10^{-10}$ mbar

286   0   0.0 ( 0 )
 نشر من قبل Lucia Hackermueller
 تاريخ النشر 2019
  مجال البحث فيزياء
والبحث باللغة English




اسأل ChatGPT حول البحث

Metal-based additive manufacturing (AM) represents a paradigm change in engineering and production methods across multiple industries and sectors. AM methods enable mass reduction and performance optimisation well beyond that achievable via conventional manufacturing, thereby impacting significantly on aerospace and space technologies. Technologies relying on high and ultra-high vacuum (UHV), such as x-ray photo-electron spectroscopy, photo-sensors, cameras and cryostats, could also benefit greatly from AM. Despite recent advances in AM processing of metals, additively manufactured UHV chambers have so far not been achieved. Reducing the mass of UHV equipment is particularly critical for the development of portable cold atom systems, which are expected to underpin the next generation of sensing and timekeeping technologies and to allow novel space-based sensors for fundamental research. We demonstrate here an additively manufactured UHV chamber reaching a pressure below $10^{-10}$ mbar, enabling a cloud of cold $^{85}$Rb atoms to be trapped - the starting point for many precision timekeeping and sensing devices. The chamber is manufactured from aluminium alloy AlSi10Mg by laser powder bed fusion and has a mass of less than a third of a commercially-available equivalent. Outgassing analysis based on mass spectrometry was performed and it was demonstrated that even without active pumping the system remains in the $10^{-9}$ mbar regime for up to 48 hours.



قيم البحث

اقرأ أيضاً

We demonstrate ultra-high Q factor microring resonators close to the intrinsic material absorption limit on lithium niobate on insulator. The microrings are fabricated on pristine lithium niobate (LN) thin film wafer thinned from LN bulk via chemo-me chanical etching without ion slicing and ion etching. A record-high Q factor up to times ten to the power of 8th at the wavelength of 1550 nm is achieved because of the ultra-smooth interface of the microrings and the absence of ion induced lattice damage, indicating an ultra-low waveguide propagation loss of about 0.28 dB per meter. The ultra-high Q microrings will pave the way for integrated quantum light source, frequency comb generation, and nonlinear optical processes.
We report about the realization of a quantum device for force sensing at micrometric scale. We trap an ultracold $^{88}$Sr atomic cloud with a 1-D optical lattice, then we place the atomic sample close to a test surface using the same optical lattice as an elevator. We demonstrate precise positioning of the sample at the $mu$m scale. By observing the Bloch oscillations of atoms into the 1-D optical standing wave, we are able to measure the total force on the atoms along the lattice axis, with a spatial resolution of few microns. We also demonstrate a technique for transverse displacement of the atoms, allowing to perform measurements near either transparent or reflective test surfaces. In order to reduce the minimum distance from the surface, we compress the longitudinal size of the atomic sample by means of an optical tweezer. Such system is suited for studies of atom-surface interaction at short distance, such as measurement of Casimir force and search for possible non-Newtonian gravity effects.
The MEG-II experiment searches for the lepton flavor violating decay: mu in electron and gamma. The reconstruction of the positron trajectory uses a cylindrical drift chamber operated with a mixture of He and iC4H10 gas. It is important to provide a stable performance of the detector in terms of its electron transport parameters, avalanche multiplication, composition and purity of the gas mixture. In order to have a continuous monitoring of the quality of gas, we plan to install a small drift chamber, with a simple geometry that allows to measure very precisely the electron drift velocity in a prompt way. This monitoring chamber will be supplied with gas coming from the inlet and the outlet of the detector to determine if gas contaminations originate inside the main chamber or in the gas supply system. The chamber is a small box with cathode walls, that define a highly uniform electric field inside two adjacent drift cells. Along the axis separating the two drift cells, four staggered sense wires alternated with five guard wires collect the drifting electrons. The trigger is provided by two 90Sr weak calibration radioactive sources placed on top of a two thin scintillator tiles telescope. The whole system is designed to give a prompt response (within a minute) about drift velocity variations at the 0.001 level.
174 - N. Korshunova , J. Jomo , G. Leko 2019
Significant developments in the field of additive manufacturing (AM) allowed the fabrication of complex microarchitectured components with varying porosity across different scales. However, due to the high complexity of this process, the final parts can exhibit significant variations in the nominal geometry. Computer tomographic images of 3D printed components provide extensive information about these microstructural variations, such as process-induced porosity, surface roughness, and other undesired morphological discrepancies. Yet, techniques to incorporate these imperfect AM geometries into the numerical material characterization analysis are computationally demanding. In this contribution, an efficient image-to-material-characterization framework using the high-order parallel Finite Cell Method is proposed. In this way, a flexible non-geometry-conforming discretization facilitates mesh generation for very complex microstructures at hand and allows a direct analysis of the images stemming from CT-scans. Numerical examples including a comparison to the experiments illustrate the potential of the proposed framework in the field of additive manufacturing product simulation.
Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. Two computational challenges have limited the applicability of TO to a variety of industrial applications. First, a TO problem often involves a large number of design variables to guarantee sufficient expressive power. Second, many TO problems require a large number of expensive physical model simulations, and those simulations cannot be parallelized. To address these issues, we propose a general scalable deep-learning (DL) based TO framework, referred to as SDL-TO, which utilizes parallel schemes in high performance computing (HPC) to accelerate the TO process for designing additively manufactured (AM) materials. Unlike the existing studies of DL for TO, our framework accelerates TO by learning the iterative history data and simultaneously training on the mapping between the given design and its gradient. The surrogate gradient is learned by utilizing parallel computing on multiple CPUs incorporated with a distributed DL training on multiple GPUs. The learned TO gradient enables a fast online update scheme instead of an expensive update based on the physical simulator or solver. Using a local sampling strategy, we achieve to reduce the intrinsic high dimensionality of the design space and improve the training accuracy and the scalability of the SDL-TO framework. The method is demonstrated by benchmark examples and AM materials design for heat conduction. The proposed SDL-TO framework shows competitive performance compared to the baseline methods but significantly reduces the computational cost by a speed up of around 8.6x over the standard TO implementation.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا