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At least 1,531 records · Page 85

Insertion Magnets

Chapter 3 in High-Luminosity Large Hadron Collider (HL-LHC) : Preliminary Design Report. The Large Hadron Collider (LHC) is one of the largest scientific instruments ever built. Since opening up a new energy frontier for exploration in 2010, it has gathered a global user community of about 7,000 scientists working in fundamental particle physics and the physics of hadronic matter at extreme temperature and density. To sustain and extend its discovery potential, the LHC will need a major upgrade in the 2020s. This will increase its luminosity (rate of collisions) by a factor of five beyond the original design value and the integrated luminosity (total collisions created) by a factor ten. The LHC is already a highly complex and exquisitely optimised machine so this upgrade must be carefully conceived and will require about ten years to implement. The new configuration, known as High Luminosity LHC (HL-LHC), will rely on a number of key innovations that push accelerator technology beyond its present limits. Among these are cutting-edge 11-12 tesla superconducting magnets, compact superconducting cavities for beam rotation with ultra-precise phase control, new technology and physical processes for beam collimation and 300 metre-long high-power superconducting links with negligible energy dissipation. The present document describes the technologies and components that will be used to realise the project and is intended to serve as the basis for the detailed engineering design of HL-LHC.

43 PARTICLE ACCELERATORS↗

Using spectral methods to obtain particle size information from optical data: applications to measurements from CARES 2010

Abstract. Multi-wavelength in situ aerosol extinction, absorption and scattering measurements made at two ground sites during the 2010 Carbonaceous Aerosols and Radiative Effects Study (CARES) are analyzed using a spectral deconvolution method that allows extraction of particle-size-related information, including the fraction of extinction produced by the fine-mode particles and the effective radius of the fine mode. The spectral deconvolution method is typically applied to analysis of remote sensing measurements. Here, its application to in situ measurements allows for comparison with more direct measurement methods and validation of the retrieval approach. Overall, the retrieved fine-mode fraction and effective radius compare well with other in situ measurements, including size distribution measurements and scattering and absorption measurements made separately for PM1 and PM10, although there were some periods during which the different methods yielded different results. One key contributor to differences between the results obtained is the alternative, spectrally based definitions of fine and coarse modes from the optical methods, relative to instruments that use a physically defined cut point. These results indicate that for campaigns where size, composition and multi-wavelength optical property measurements are made, comparison of the results can result in closure or can identify unusual circumstances. The comparison here also demonstrates that in situ multi-wavelength optical property measurements can be used to determine information about particle size distributions in situations where direct size distribution measurements are not available.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Mapping the performance of a versatile water-based condensation particle counter (vWCPC) with numerical simulation and experimental study

Accurate airborne aerosol instrumentation is required to determine the spatial distribution of ambient aerosol particles, particularly when dealing with the complex vertical profiles and horizontal variations of atmospheric aerosols. A versatile water-based condensation particle counter (vWCPC) has been developed to provide aerosol concentration measurements under various environments with the advantage of reducing the health and safety concerns associated with using butanol or other chemicals as the working fluid. However, the airborne deployment of vWCPCs is relatively limited due to the lack of characterization of vWCPC performance at reduced pressures. Given the complex combinations of operating parameters in vWCPCs, modeling studies have advantages in mapping vWCPC performance. In this work, we thoroughly investigated the performance of a laminar-flow vWCPC using COMSOL Multiphysics® simulation coupled with MATLAB™. We compared it against a modified vWCPC (vWCPC model 3789, TSI, Shoreview, MN, USA). Our simulation determined the performance of particle activation and droplet growth in the vWCPC growth tube, including the supersaturation, $D$ p,kel,0 (smallest size of particle that can be activated), $D$ p,kel,50 (particle size activated with 50 % efficiency) profile, and final growth particle size D d under wide operating temperatures, inlet pressures $P$ (30–101 kPa), and growth tube geometry (diameter $D$ and initiator length $L$ ini ). The effect of inlet pressure and conditioner temperature on vWCPC 3789 performance was also examined and compared with laboratory experiments. The COMSOL simulation result showed that increasing the temperature difference (Δ$T$) between conditioner temperature $T$ con and initiator $T$ ini will reduce $D$ p,kel,0 and the cut-off size $D$ p,kel,50 of the vWCPC. In addition, lowering the temperature midpoint ($T$ mid = $\frac{T_{con}+T_{ini}}{2}$ increases the supersaturation and slightly decreases the $D$ p,kel . The droplet size at the end of the growth tube is not significantly dependent on raising or lowering the temperature midpoint but significantly decreases at reduced inlet pressure, which indirectly alters the vWCPC empirical cut-off size. Our study shows that the current simulated growth tube geometry ($D$=6.3 mm and $L$ ini =30 mm) is an optimized choice for current vWCPC flow and temperature settings. The current simulation can more realistically represent the $D$ p,kel for 7 nm vWCPC and also achieved good agreement with the 2 nm setting. Using the new simulation approach, we provide an optimized operation setting for the 7 nm setting. This study will guide further vWCPC performance optimization for applications requiring precise particle detection and atmospheric aerosol monitoring.

47 OTHER INSTRUMENTATION↗

Operation of the Airmodus A11 nano Condensation Nucleus Counter at various inlet pressures and various operation temperatures, and design of a new inlet system

Measuring sub-3 nm particles outside of controlled laboratory conditions is a challenging task, as many of the instruments are operated at their limits and are subject to changing ambient conditions. In this study, we advance the current understanding of the operation of the Airmodus A11 nano Condensation Nucleus Counter (nCNC), which consists of an A10 Particle Size Magnifier (PSM) and an A20 Condensation Particle Counter (CPC). The effect of the inlet line pressure on the measured particle concentration was measured, and two separate regions inside the A10, where supersaturation of working fluid can take place, were identified. The possibility of varying the lower cut-off diameter of the nCNC was investigated; by scanning the growth tube temperature, the range of the lower cut-off was extended from 1–2.5 to 1–6 nm. Here we present a new inlet system, which allows automated measurement of the background concentration of homogeneously nucleated droplets, minimizes the diffusion losses in the sampling line and is equipped with an electrostatic filter to remove ions smaller than approximately 4.5 nm. Lastly, our view of the guidelines for the optimal use of the Airmodus nCNC is provided.

54 ENVIRONMENTAL SCIENCES↗

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

Aubourg, Eric [APC, Paris] (ORCID:000000025592023X↗

Size-resolved ice nucleating particle (INP) concentrations from the MOSAiC campaign

Sample collection: Ground-based immersion mode ice nucleation measurements were conducted on samples collected at the U.S. DOE ARM AMF2 onboard the R/V Polarstern P-deck during MOSAiC from 23 Oct 2019 to 01 Oct 2020. Samples were collected using a Davis Rotating-drum Unit for Monitoring (DRUM; DRUMAir, LLC) cascading impactor in 4 stages (i.e., size bins). Instrument information on the model used, the DA400, can be found at the DRUMAir website (http://www.drumair.com/). Stages A, B, C, and D had particle size cuts at 2.96, 1.21, 0.34, and 0.15 &micro;m in particle diameter, respectively. Strips of sample substrate (PFA, perfluoroalkoxy; McMaster) were adhered to each disc in each stage and coated with petrolatum to enable particles to stick on impact. The discs move slowly over time, such that aerosol loading is streaked onto the PFA. Every 24 hours, a blank is created on the PFA to separate daily samples. During MOSAiC, the pump typically pulled 20 &ndash; 35 lpm (average of 32 lpm) at the inlet with all the discs in place in each stage. Daily samples were collected for each of the four stages, equaling 28,800 &ndash; 50,400 total L of air (average of 46,080 L total L of air) per sample. Discs rotated for approximately 24 days before the sampling per strip was complete and changed to a new disc. Discs were stored frozen after collection. Sample preparation for analysis: Following collection and transport from MOSAiC to the Kreidenweis/DeMott laboratory, care of PI Creamean, in Fort Collins, CO, strips were removed from the discs and cut up to separate daily samples. Each daily portion of the strip was stored in separate sterile 15-mL centrifuge tubes and stored in a standard commercial freezer until analysis (approximately 4 &ndash; 12 months after collection). Sample analysis: Drop freezing assays (DFAs) were conducted on one daily sample set every 3 &ndash; 4 days of the study for all four stages using the Colorado State University (CSU) cold plate (CP). Sample set days were chosen to cover the entire year of MOSAiC and avoiding days where the AOS purge system was activated. Immediately after removal from the freezer, 2 mL of 0.1-&micro;m filtered deionized water was added with a single-use sterile syringe directly to the tubes, then mixed using a vortex mixer for at least 30 minutes at 500 rpm to resuspend particles from the PFA into the deionized water. Following sample preparation, another single-use syringe was used to create 100 aliquots on a 3-inch diameter copper plate, then covered with a transparent plastic dome. The drops were approximately the same volume (2.5 &micro;L each), though careful inspection. The plate was cooled at approximately 3 &ndash; 10 K min-1 from room temperature until all drops froze on the plate. Temperature was measured by an Omega thermocouple probes and meter. Drop freezing was visually detected but recorded through monitoring software to provide the time frozen, channel of the meter used, temperature, and cooling rate for each drop. Not all 100 drops were always detected; the total number of rows in each data file equals the number of drops recorded, which typically was > 80 %. Each sample was tested three times (i.e., a new set of 100 drops was created for each test). Data provided are files containing these parameters. From the freezing temperatures recorded, the fraction frozen was and INP concentrations were calculated per L of air using the equation from Vali (1971): [INPs] = &minus;(ln(f)/Valiquot)*(Vsuspension/Vair) where f is the proportion of droplets not frozen, Valiquot is the volume of each aliquot (i.e., 2.5e-6 L), Vsuspension is the volume of the suspension (i.e., 0.002 L), and Vair is the volume of air per sample (i.e., 36000 L). Data usage guidance: There are three files included with this dataset: (1) dumcp_inp_concentrations.csv which includes cumulative INP concentrations binned every 0.5 &deg;C from 0 to &ndash;30 &deg;C for all samples and size ranges (i.e., DRUM stages) processed via DFA using the CSU CP; (2) dumcp_inp_standard_deviation.csv which includes the standard deviations for all samples and temperatures in dumcp_inp_concentrations.csv; and (3) dumcp_blank_concentrations_standard_deviation.csv which includes the INP concentrations and standard deviations for the deionized water and PFA blanks binned every 0.5 &deg;C from 0 to &ndash;30 &deg;C. The first column in each file represents the freezing temperature in &deg;C binned every 0.5 &deg;C from 0 to &ndash;30 &deg;C (first cell in this column = header = &ldquo;temp&rdquo;). The second column in each file represents the standard deviation of each temperature bin in &deg;C (first cell in this column = header = &ldquo;temperr&rdquo;). For file (1) dumcp_inp_concentrations.csv, the remaining columns indicate the INP concentrations per L of air for each temperature bin. For each sample column, the header in the first row indicates the stage (i.e., size bin as &ldquo;A_&rdquo;, &ldquo;B_&rdquo;, &ldquo;C_&rdquo;, or &ldquo;D_&rdquo;) and date (&ldquo;_yyyymmdd) of collection. For file (2) dumcp_inp_standard_deviation.csv, the remaining columns indicate the INP concentration standard deviation per L of air for each temperature bin. For each sample column, the header in the first row indicates the stage (i.e., size bin as &ldquo;A_&rdquo;, &ldquo;B_&rdquo;, &ldquo;C_&rdquo;, or &ldquo;D_&rdquo;) and date (&ldquo;_yyyymmdd) of collection. For file (3) dumcp_blank_concentrations_standard_deviation.csv, the remaining columns indicate the INP concentrations and standard deviations for the deionized water blank (&ldquo;di_0.1&rdquo; and &ldquo;di_0.1sd&rdquo; for the concentrations and standard deviations, respectively) and PFA blank (&ldquo;pfa&rdquo; and &ldquo;pfasd&rdquo; for the concentrations and standard deviations, respectively). Please see Creamean et al. (2019a; 2018a; 2018b; 2019b) for additional details on DRUM sample collection and offline processing. For questions or help with data interpretation, contact Dr. Jessie Creamean (jessie.creamean@colostate.edu). References: Creamean, J. M., Mignani, C., Bukowiecki, N., and Conen, F.: Using spectra characteristics to identify ice nucleating particle populations during winter storms in the Alps, Atmos. Chem. Phys., 19, 8123&ndash;8140, 2019a. Creamean, J. M., Kirpes, R. M., Pratt, K. A., Spada, N. S., Maahn, M., de Boer, G., Schnell, R. C., and China, S.: Marine and terrestrial influences on ice nucleating particles during continuous springtime measurements in an Arctic oilfield location, Atmospheric Chemistry and Physics, 18, 18023-18042, 10.5194/acp-18-18023-2018, 2018a. Creamean, J. M., Primm, K. M., Tolbert, M. A., Hall, E. G., Wendell, J., Jordan, A., Sheridan, P. J., Smith, J., and Schnell, R. C.: HOVERCAT: A novel aerial system for evaluation of aerosol-cloud interactions, Atmospheric Measurement Techniques, 11, 3969-3985, 10.5194/amt-11-3969-2018, 2018b. Creamean, J. M., Cross, J. N., Pickart, R., McRaven, L., Lin, P., Pacini, A., Hanlon, R., Schmale, D. G., Ceniceros, J., Aydell, T., Colombi, N., Bolger, E., and DeMott, P. J.: Ice nucleating particles carried from below a phytoplankton bloom to the Arctic atmosphere, Geophys. Res. Lett., 46, 8572-8581, https://doi.org/10.1029/2019GL083039, 2019b. Vali, G.: Quantitative Evaluation of Experimental Results an the Heterogeneous Freezing Nucleation of Supercooled Liquids, Journal of the Atmospheric Sciences, 28, 402-409, 10.1175/1520-0469(1971)028<0402:qeoera>2.0.co;2, 1971.

4-stage DRUM aerosol collector (DA400),Ice nuclei,↗

Size_resolved_ice_nucleating_particle_INP_concentrations_from_the_INPOP_campaign

Ground-based immersion mode ice nucleation measurements were conducted on samples collected at the US DOE ARM AMF3 in Oliktok Point, AK from 1 Mar to 31 May 2017. Samples were collected using a Davis Rotating-drum Unit for Monitoring (DRUM; DRUMAir, LLC) cascading impactor in 4 stages (i.e., size bins). Instrument information on the model used, the DA400, can be found at the DRUMAir website (http://www.drumair.com/). Stages A, B, C, and D, had particle size cuts at 2.96, 1.21, 0.34, and 0.15 &micro;m in particle diameter. Strips of sample substrate (PFA, perfluoroalkoxy; McMaster) were adhered to each disc in each stage and coated with petrolatum to enable particles to stick on impact. The discs move slowly over time, such that aerosol loading is streaked onto the PFA. Every 24 hours, a blank is created on the PFA to separate daily samples. The pump typically pulled 25 lpm at the inlet with all the discs in place in each stage. Daily samples were collected for each of the four stages, equaling 36,000 total L of air per sample. Discs rotated for approximately 24 days before the sampling per strip was complete and changed to a new disc. Discs were stored frozen after collection. Following collection and transport from AMF3 to PI Creamean's laboratory in Boulder, CO, strips were removed from the discs and cut up to separate daily samples. Each daily portion of the strip was stored in separate sterile bags and stored in a standard commercial freezer until analysis (approximately 4 &ndash; 5 months after collection). Drop freezing assays (DFAs) were conducted on one daily sample per week of the study for all four stages, with daily samples analyzed from 22 - 29 May 2017. Immediately after removal from the freezer, 2 mL of ultrapure water was added with a single-use sterile syringe directly to the bags, then mixed using a vortex mixer for 2 hours at 500 rpm to resuspend particles from the PFA into the ultrapure water. Following sample preparation, another single-use syringe was used to create 100 aliquots on a 3-inch diameter copper plate, then covered with a transparent plastic dome. The drops were approximately the same volume (2.5 &micro;L each), though careful inspection. The plate was cooled at approximately 3 &ndash; 10 K min-1 from room temperature until all drops froze on the plate. Temperature was measured by an Omega thermocouple probes and meter. Drop freezing was visually detected, but recorded through monitoring software to provide the time frozen, channel of the meter used, temperature, and cooling rate for each drop. Not all 100 drops were always detected; the total number of rows in each data file equals the number of drops recorded, which typically was > 80 %. Each sample was tested three times (i.e., a new set of 100 drops was created for each test). Data provided are files containing these parameters. From the freezing temperatures recorded, the fraction frozen can be calculated as can the estimated INP concentration per L of air using the equation from Vali (1971): [INPs] = &minus;(ln(f)/Valiquot)*(Vsuspension/Vair) where f is the proportion of droplets not frozen, Valiquot is the volume of each aliquot (i.e., 2.5e-6 L), Vsuspension is the volume of the suspension (i.e., 0.002 L), and Vair is the volume of air per sample (i.e., 36000 L). Vali, G.: Quantitative Evaluation of Experimental Results an the Heterogeneous Freezing Nucleation of Supercooled Liquids, Journal of the Atmospheric Sciences, 28, 402-409, 1971.

4-stage DRUM aerosol collector (DA400),Aerosol imp↗

Aerosol Microphysics and Chemical Measurements at Mt. Soledad and Scripps Pier during the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) from February 2023 to February 2024 UCSD Library Collection

This dataset includes guest instrument measurements and other PI products for aerosol microphysics and chemical measurements collected at Mt. Soledad and Scripps Pier during the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) from February 2023 to February 2024. The measurements include the following instruments at Mt. Soledad: High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS, Aerodyne), Scanning Electrical Mobility Spectrometer (SEMS, Brechtel Manufacturing Inc.), Aerodynamic Particle Sizer (APS, Droplet Measurements Technologies), Single Particle Soot Photometer (SP2, Drople Measurements Technologies), Meteorological Station (WXT520, Vaisala), Ozone (Teco), and trace gas proxies (Teledyne). In addition, the analyses of particle filters collected at Mt. Soledad for three dry-diameter size cuts (<1 micron, <0.5 micron, <0.18 micron) and at Scripps Pier for one dry-diameter size cut (<1 micron) by Fourier Transform Infrared (FTIR) and X-ray Fluorescence (XRF) are reported. A differential mobility analyzer operated as a scanning mobility particle sizer (SMPS, TSI Inc.), a printed particle optical spectrometer (POPS, Grimm), and a continuous flow diffusion cloud condensation nuclei (CCN, DMT) counter provide the mobility aerosol size distribution (30-360 nm), optical size distribution (150 - 6000 nm), size-resolved CCN distribution (30-360 nm) at 0.2, 0.4, 0.6, 0.8, and 1.0% supersaturation. Measurements are reported for both sampling from an isokinetic aerosol inlet and from a Counterflow Virtual Impactor (CVI, Brechtel Manufacturing Inc.). The data are available at the following link: https://library.ucsd.edu/dc/collection/bb0898306q

54 ENVIRONMENTAL SCIENCES↗

Effect of Ionic Core on the Properties of Non-Isothermal Plasmas

This study investigates the effects of ionic cores on non-isothermal plasmas using a novel ion-ion interaction potential that incorporates screening effects from both ion cores and exchange-correlation interactions. Our findings indicate that with increasing distance, the effective potential approaches a Yukawa-like screening potential, while at shorter distances, strong electron binding weakens screening. The different values of the cutoff radius ' cut and core edge steepness α significantly influence the potential behavior and radial distribution functions (RDFs). Higher coupling parameters (Γ i ) strengthen the electron-ion interactions, leading to deeper potential wells and more pronounced non-ideality corrections. Increasing Θ decreases the absolute values of non-ideality corrections, indicating fewer interactions in the system. A larger cutoff radius r cut due to weaker screening effects. As Γ i at a fixed parameter also reduces corrections increases, non-ideality corrections grow, reflecting stronger coupling. The results show the importance of taking into account the ion core effects in dense non-isothermal plasma research.

dense plasmas↗

SITCOMTN-162: Testing the implementation of Metadetection and Cell-Based Coadds on Abell 360 LSSTComCam data

The purpose of this technote is to test the technical quality of LSSTComCam commissioning data, specifically the Rubin_SV_38_7 field, by utilizing cell-based coadds and Metadetection by measuring the tangential and cross weak lensing shear profiles of the massive cluster Abell 360 (called A360 throughout the technote). The process entails generating the cell-based coadds for Metadetection to run on, identifying and removing cluster member galaxies, applying quality cuts, calibrating the shear measurements, and validation. Cell-based coadds and Metadetection are both currently in the process of being implemented within the LSST Science Pipelines at the time of this technote. There is substantial technical value in attempting a difficult measurement prior to full implementation. Measuring the tangential shear around A360 will showcase the current abilities of these algorithms, as well as highlight where work is still needed. As seen from the resulting shear profile of A360, the cell-based coadds and Metadetection are able to work in tandem to produce a shear catalog and resulting reduced shear profile. This technote is one part of a series studying A360 in order to both stress test the commissioning camera and demonstrate the technical capabilities of the Vera Rubin Observatory. We study the quality of the PSF modeling and impact it can have on cluster WL in [Combet et al., 2025], implementation of cell-based coadds and subsequent use for Metadetect [Sheldon et al., 2023] in this technote, photometric calibration in (in prep), source selection and photometric redshifts in [Adari et al., 2025], use of Anacal [Li et al., 2024] to produce a cluster shear profile in [Li et al., 2025], and background subtraction in this field and Fornax in [Zhou et al., 2025].

79 ASTRONOMY AND ASTROPHYSICS↗

Testing convolutional neural network based deep learning systems: a statistical metamorphic approach

Machine learning technology spans many areas and today plays a significant role in addressing a wide range of problems in critical domains,i.e., healthcare, autonomous driving, finance, manufacturing, cybersecurity,etc. Metamorphic testing (MT) is considered a simple but very powerful approach in testing such computationally complex systems for which either an oracle is not available or is available but difficult to apply. Conventional metamorphic testing techniques have certain limitations in verifying deep learning-based models (i.e., convolutional neural networks (CNNs)) that have a stochastic nature (because of randomly initializing the network weights) in their training. In this article, we attempt to address this problem by using a statistical metamorphic testing (SMT) technique that does not require software testers to worry about fixing the random seeds (to get deterministic results) to verify the metamorphic relations (MRs). We propose seven MRs combined with different statistical methods to statistically verify whether the program under test adheres to the relation(s) specified in the MR(s). We further use mutation testing techniques to show the usefulness of the proposed approach in the healthcare space and test two CNN-based deep learning models (used for pneumonia detection among patients). The empirical results show that our proposed approach uncovers 85.71% of the implementation faults in the classifiers under test (CUT). Furthermore, we also propose an MRs minimization algorithm for the CUT, thus saving computational costs and organizational testing resources.

Computer Science↗

Rock bit requires no flushing medium to maintain drilling speed

Steel drill bit having terraces of teeth intersected by spiral grooves with teeth permits the boring of small holes through rock with low power. The cuttings are stored in a chamber behind the cutting head. Could be used as sampling device.

Source record↗

Portable tool removes burrs from pipe and tubing

Portable tool cleanly removes burrs that remain on tubing when it is cut. It restores the cut end to its original configuration, and carries away all chips and pieces. This tool is used in places of limited access where a larger tool could not be used.

Headley, C. A.↗

Modified drill permits one-step drilling operation

Drill with modified cutting faces permits one-step drilling operation without chatter upon contact and premature wear. The modification of the drill, which has the same diameter as that of the desired hole, consists of a groove across the bottom of each of the cutting faces of the drill flutes.

Libertone, C.↗

Tool separates sleeve-type unions without heat

Tool that uses conventional milling and cutting techniques separates sleeve type tubing unions and tubes without using heat. A selection of holders, associated bits, and cutting wheels permits preparation of varied diameter unions.

Millett, A. U.↗

Hole saw drill attachment has zero force reaction

Zero reaction tools require no force application by workers in space. The tool accomplishes hole cutting by holding the workpiece and feeding the cutting blade into and through it by forces entirely absorbed within the tool.

Holmes, A. E.↗

Cold machining of high density tungsten and other materials

Cold machining process, which uses a sub-zero refrigerated cutting fluid, is used for machining refractory or reactive metals and alloys. Special carbide tools for turning and drilling these alloys further improve the cutting performance.

Ziegelmeier, P.↗