Forward Part Research Articles

Triple GEM detectors for the Phase-2 upgrade of the CMS experiment at the LHC

The High-Luminosity LHC (HL-LHC) will increase proton-proton collision rates to 5–7.5 times the nominal LHC luminosity, with an expected number of 140–200 pp-interactions per bunch crossing (Pile-up or PU). To maintain the performance of muon triggering and reconstruction under high background, the forward part of the Muon spectrometer of the CMS experiment will be upgraded with Gas Electron Multiplier (GEM) and improved Resistive Plate Chamber (iRPC) detectors. A first GEM station (GE1/1) was installed during Long Shutdown 2 (LS2, 2019–2021) while second and third stations (GE2/1 and ME0) will be installed in the third Long Shutdown (LS3, 2026–2028) and during the Run 4 technical stops. GE1/1, considered an early Phase-2 upgrade, will reduce the p T threshold by combining GEM and Cathode Strip Chamber (CSC) hits in the forward muon system at twice the LHC design luminosity (2 × 1034/cm2 s, 50 PU). The commissioning of the GE1/1 detector has been completed during Run 3 in 2022 and 2023. Most chambers are operating stably with an efficiency higher than 95%. Demonstration of the combined CSC-GEM trigger will take place in 2024. The first GE2/1 detectors were installed in the LHC 2023–24 technical stop and the ME0 detector is entering the production phase in 2024. This presentation discusses the commissioning and early performance of GE1/1, together with the lessons learnt which were used to improve the detector and electronics design for GE2/1 and ME0. We also present the R&D performed to optimize the ME0 detector for its high-rate environment, together with the performance measurements of the first stack of ME0 detectors for standalone muon segment reconstruction.

R&D of the cluster finding algorithm for CMS iRPC detector

The Compact Muon Solenoid (CMS) experiment will undergo phase-II upgrade to enhance the capacity of detectors in the High-Luminosity Large Hadron Collider era. An important extension involves the installation of the Improved Resistive Plate Chambers (iRPC) in the most forward part of the endcap muon system. The iRPC detector addresses the efficiency drop experienced by the current CMS trigger system for single muon triggers in the high pseudorapidity region. It features a double-ended read-out method, allowing the determination of the hit position along the strip using the time difference between signals from both ends. This feature provides precise spatial information for clustering. To utilize this feature and integrate iRPC cluster information into the CMS trigger system, this paper proposes a 2-Dimensional (2D) cluster-finding algorithm. This algorithm provides the 2D coordinates to CMS trigger system, with each cluster being timestamped to reduce time ambiguity in endcap trajectory reconstruction. The proposed algorithm was implemented in the firmware of the iRPC Back-End Electronics and evaluated using a dedicated cosmic muon setup. The results show a position precision of 2.55 cm and an algorithm efficiency better than 99%, demonstrating the algorithm's potential to improve muon detection and trigger efficiency in the CMS endcap region.

Laminar Boundary Layer over a Serrated Backward-Facing Step

Laminar flow over a modified backward-facing step (BFS) was studied experimentally and computationally, with the results compared to a flight test on a Piper Cherokee wing. The BFS was modified with a serrated spanwise variation while maintaining a constant step height, and this modification is termed a serrated BFS (sBFS). A scaling law was proposed and then used to develop the experimental operation conditions. The experiments showed evidence that the transition to turbulence was delayed over the forward part of the serration (termed the valley). The boundary layer growth and characterization were used to validate the computational model, which was then used to examine details not available from the experiment, including the wall shear stress distribution and streamlines as they go over the sBFS. The wall shear stress showed the formation of low-shear diamonds downstream of the sBFS valley that were associated with laminar flow, which confirmed previous assumptions about the low-shear diamonds observed in the flight tests. The length of the low-shear diamonds was scaled with the sBFS geometry. Finally, the streamlines showed that the near-wall flow forward of the sBFS is pumped towards the sBFS peak, where it rapidly transitions to turbulence at that location.

DG-based joint transmission-reflection traveltime tomography and its application of borehole seismic

Abstract The limitations of the coverage range and density of transmission wave often result in less-than-ideal results in traveltime tomography. In contrast, joint transmission-reflection traveltime tomography can not only recover deep structures that transmission tomography cannot detect but also optimize its inversion results. In this article, we perform joint tomography on borehole seismic (VSP, RVSP and crosswell seismic) data to obtain near-wellbore structures. In the forward part, we solve the factored equation by the discontinuous Galerkin (DG) method to calculate the transmission/reflection traveltime. Due to the large wavefront curvature near the source point, the traveltime errors generated by the numerical simulation will propagate from the source to all the calculation domains. According to the factorization principle, the equation solution is decomposed into two parts to solve the point-source singularity. To further improve the accuracy of solving traveltime, we use the DG method to solve the factored eikonal equation with additive factors (the factored DG method), obtaining second-order accuracy solution. The adjoint-state method is employed in the inversion section to calculate the gradient of the misfit function. And we use the traveltime difference observed inside the model to define the misfit function, which is more suitable for borehole seismic and avoids the influence of surface normal vectors on gradients. Numerical tests applied on models indicate that the joint tomography method has the potential to accurately inverse the seismic structure information near the well and recover the deep underground structure.

Design validation of the CMS Phase-2 Triple-GEM detectors

The High-Luminosity LHC will deliverproton-proton collisions at 5.0–7.5 times the nominal LHC luminosity, with anexpected number of 140–200 pp-interactions per bunch crossing. To maintain the performance of muon triggering and reconstruction underhigh background, the forward part of the Muon spectrometer of the CMSexperiment will be upgraded with Gas Electron Multipliers (GEM) and improvedResistive Plate Chambers detectors. Particularly challenging isthe extension of the pseudorapidity of the muon system up to |η| < 2.8with a 6-layer station, named ME0, that will be installed behind the newhigh granularity calorimeter and that will see particle rates up to150 kHz/cm2 and integrate a dose of 250 krad by the end of the High Luminosity phase of the LHC.In this contribution we will present the major design changes of the Triple-GEMdetectors to adapt them for the harsh radiation environment and the highparticle rate. We shall summarize the performance of prototype Triple-GEMdetectors measured with X-rays and gamma at theCERN GIF++ irradiation facility.

Deep learning model for dynamic color design of all-dielectric metasurfaces.

Silicon nanostructure colors have rapidly developed in recent years, offering high resolution and a broad color gamut that traditional pigments cannot achieve. The reflected colors of metasurfaces are determined by the geometric structure of the unit cell and the refractive index matching layer parameters. It is evident that the design of specific colors involves numerous parameters, making it challenging to achieve through conventional calculations. Therefore, the tandem network instead of conventional electromagnetic simulation is natural. The forward part of the network incorporates feature cross terms to improve accuracy, enabling high-precision predictions of structural colors based on structural parameters. The average color difference between the predicted and actual color values in the L,a,b color space is 1.38. The network has been proven to accurately predict the refractive index and height of the refractive index matching layer during the dynamic tuning process. Additionally, the issue of the inverse network converging to incorrect solutions was addressed by leveraging the characteristics of the activation function. The results show that the color difference between the colors designed by the inverse network compared to the actual colors in the L,a,b color spaces is only 2.22, which meets the requirements for commercial applications.

Monte Carlo Full-Waveform Inversion of Cross-Hole Ground-Penetrating Radar Data Based on Improved Residual Network

A full-waveform inversion (FWI) of ground-penetrating radar (GPR) data can be used to effectively obtain the parameters of a shallow subsurface. Introducing the Markov chain Monte Carlo (MCMC) algorithm into the FWI can reduce the dependence on the initial model and obtain the global optimal solution, but it requires a large number of computations. In order to better detect underground targets based on ground-penetrating radar data, this paper proposes a joint scheme of an improved ResNet and an MCMC full-waveform inversion. This scheme combines the Bayesian MCMC algorithm and an improved ResNet to accurately invert the target dielectric permittivity. The introduction of deep learning networks into the forward calculation part of the MCMC inversion algorithm replaced the complex forward simulation process, greatly improving the inversion speed. It is worth noting that the neural network model was an approximation of complex forward modeling, and, therefore, it contained modeling errors. The MCMC method could quantify and explain modeling errors during the inversion process, reducing the impact of modeling errors on the inversion results. Finally, a simulation dataset was constructed for training and testing, and the errors were statistically analyzed. The results showed that this method can accurately reconstruct underground medium targets, and it has strong robustness and efficiency.

Probabilistic seismic inversion based on physics-guided deep mixture density network

Deterministic inversion based on deep learning has been widely utilized in model parameters estimation. Constrained by logging data, seismic data, wavelet and modeling operator, deterministic inversion based on deep learning can establish nonlinear relationships between seismic data and model parameters. However, seismic data lacks low-frequency and contains noise, which increases the non-uniqueness of the solutions. The conventional inversion method based on deep learning can only establish the deterministic relationship between seismic data and parameters, and cannot quantify the uncertainty of inversion. In order to quickly quantify the uncertainty, a physics-guided deep mixture density network (PG-DMDN) is established by combining the mixture density network (MDN) with the deep neural network (DNN). Compared with Bayesian neural network (BNN) and network dropout, PG-DMDN has lower computing cost and shorter training time. A low-frequency model is introduced in the training process of the network to help the network learn the nonlinear relationship between narrowband seismic data and low-frequency impedance. In addition, the block constraints are added to the PG-DMDN framework to improve the horizontal continuity of the inversion results. To illustrate the benefits of proposed method, the PG-DMDN is compared with existing semi-supervised inversion method. Four synthetic data examples of Marmousi II model are utilized to quantify the influence of forward modeling part, low-frequency model, noise and the pseudo-wells number on inversion results, and prove the feasibility and stability of the proposed method. In addition, the robustness and generality of the proposed method are verified by the field seismic data.

Analysis of the ships’s bow to minimise air pollution – case study of the X-bow ship

ABSTRACT Air pollution is a major concern in the maritime industry because of its negative health impacts and greenhouse gas emission. Reducing the friction and resistance on the hull of a marine vessel improves the efficiency. It also reduces fuel consumption and carbon emissions in the atmosphere. Researches show that the design of the forward part of the hull has an effect on the total hull resistance. This paper explains how to reduce air pollution from an X-bow ship by analysing the ship’s bow and making essential modifications. The analysis of the front section of an X-bow ship demonstrated how the flare angle and stem angle have an effect on the total hull resistance. The model of the ship was created by using the Rhinoceros software, and the CFD analysis was done in Autodesk CFD software. Based on the results, it was found that the X-bow model of (10° stem, 10°flare) has optimal ship performance between the range speed of 8-14 knots as compared with the original X-bow ship of (6° stem, 21° flare). The new model (10° stem, 10°flare) requires an Effective Horse Power (EHP) of 2847.97 HP which is much lower than the power of the original model,3947.56HP.

P14 A rare adverse event of eosinophilic fasciitis secondary to immune checkpoint inhibitor therapy

P14 A rare adverse event of eosinophilic fasciitis secondary to immune checkpoint inhibitor therapy

GEM detectors for the CMS endcap muon system: status of three new detector stations

The High-Luminosity LHC (HL-LHC, or Phase-2 LHC) will deliver proton-proton collisions at 5–7.5 times the nominal LHC luminosity, with an expected number of 140–200 pp-interactions per bunch crossing (Pile-up or PU). To maintain the performance of muon triggering and reconstruction under high background, the forward part of the Muon Spectrometer of the CMS experiment will be upgraded with Gas Electron Multipliers (GEM) and improved Resistive Plate Chambers (iRPC) detectors [1,2]. A first GEM station (GE1/1) was installed during Long Shutdown 2 (LS2, 2019–2021), a 2nd station (GE2/1) of Triple-GEM detectors will be installed in winter 2023–24 and 2024–25, while a new 6-layer station (ME0) will be installed in the third Long Shutdown (LS3, 2026–2028). GE11 is considered an early Phase-2 upgrade as it will reduce the pT threshold by combining GEM and Cathode Strip Chamber (CSC) hits in the forward muon system at twice the LHC design luminosity (ℒ = 2 · 1034 cm-2 s-1, 50 PU). After a successful start of Run-3 in 2022, with almost 40 fb-1 collected, the commissioning of the GE1/1 detector is nearly complete. Most chambers are operated stably with an efficiency in excess of 95%, next being the demonstration of the combined CSC-GEM trigger in 2023. The lessons learnt with the first large-area GEM station have lead to improvements in detector and electronics design for the Phase 2 detectors GE2/1 and ME0. This proceeding will discuss the progress made since last MPGD Conference (MPGD 2019) [3], discussing the commissioning and early performance of GE1/1; the design improvements and start of construction of GE2/1; and the R&D currently ongoing for ME0.

A new concept of diminishing cavitation inception number for hydrofoils

A new concept of diminishing cavitation inception number is applied to sheet cavitation over contours traditional for sections of marine propeller blades. An inverse hydrodynamic problem is solved for a segment located downstream from the point of separation of laminar boundary layer at highest angles of attack of the contour. The pressure along the forward part of this segment will be constant and it will have a prescribed increase along its rear part up to the point of merging the corrected streamline with the original contour. Description of employed numerical method is supplemented by examples of enhancement of traditional hydrofoils. The substantial improvement of their baskets of minimal pressure is obtained.

Dynamic Stall Investigation on a Rotating Semielastic Double-swept Rotor Blade at the Rotor Test Facility Göttingen

Experimental investigations of three-dimensional dynamic stall on a four-bladed Mach-scaled semielastic rotor with an innovative double-swept rotor blade planform are presented. The study focuses on the coupling between the aeroelastic behavior of the blade and the underlying aerodynamics. Blade bending moment and flap displacement measurements were conducted using strain gauges and optical tracking of blade tip markers. The aerodynamic behavior was characterized by means of unsteady surface pressure measurements using unsteady pressure-sensitive paint (iPSP) across the outer 65% of the blade span and fast response pressure transducers at discrete locations. Different cyclic-pitch settings were investigated at a rotation frequency of frotor = 23.6 Hz that corresponds to blade tip Mach and Reynolds numbers of Mtip = 0.282– 0.285 and Retip = 5.84-5.95 ×10 5. The findings reveal a detailed insight into the nonlinear behavior in the flap movement during downstroke. iPSP and pressure transducer data indicate that this nonlinear flap behavior is caused by a radially phase-shifted dynamic stall process at the forward and backward swept part of the blade.

Guidance and Control of Spin-Stabilized Projectiles Based on Super Twisting Algorithm

In this paper, a hybrid integrated guidance and control design exploiting the time-scale separation principle for a canard-guided dual-spin projectile (DSP) is proposed. The DSP consists of two parts: i) the aft part, which has a high spin rate for providing gyroscopic and aerodynamic stability, and ii) the forward part, which contains a course correction fuze that houses two pairs of movable canards to ensure precise interception. The guidance and control algorithms operate in a two-loop structure where the outer guidance loop generates required pitch and yaw rate commands, whereas the inner loop tracks these commands by generating canard deflections. The rolling motion of the forward part is controlled using a coaxial motor. The multibody dynamic model of DSP has seven degrees of freedom and is highly coupled and nonlinear. For the guidance, a finite horizon optimal control problem is formulated as an output regulation problem with output vector taken as pitch and yaw components of zero effort miss vector. A coupled pitch–yaw autopilot for pitch and yaw rate command tracking and a roll autopilot for roll stabilization of the forward part are designed based on super twisting algorithm. The efficacy of the control design is shown through numerical simulations.

New Data Processing Method for Acoustic Emission Signals for Metalworking Fluid Classification

Metalworking fluid (MWF) differentiating is of important significance as their additives affect metalworking process extremely so the evaluation of MWF should be suitable conducted. Acoustic Emission (AE) measurements can be easily taken as process-close measures for evaluating cutting and forming processes as well as MWFs performance. In thread forming process, AE measurements from different kinds of MWFs could be – related to the position of the tool - divided into different process phases: air, forward, and reverse phases. From physical view, forward part contains most useful AE data. Therefore, extracting forward part data from the overall signal is significant for MWFs classification. Because boundaries among different parts in time domain of AE signals are not clear, in this contribution, a new data processing method is proposed to abstract the forward part data from non-related parts of the whole measurement signal. For the first time, scalogram is applied to find the boundaries in time domain as an aid. Firstly, the intact measurement signal is transformed from time domain to time-frequency domain by continuous wavelet transform (CWT) and scalogram is acquired. As boundaries among different phases are obvious in scalogram, by reverse calculation, boundaries’ location in time domain could be defined and forward part data of each measurement are picked out. Afterwards, data in forward phase are divided into different samples and each sample contains data of one round. Finally, samples’ features are extracted and classified by convolutional neural network (CNN). By adjusting CNN structure and hyperparameters with cross validation method, features in time domain could be distinguished well. For five kinds of testing MWF, the classification accuracy is as high as 98.11 %. For reference, oil-based, and water-based MWF classification, the results can reach to 98.94 %. Accuracy for water-based MWF distinction is 97.55 % while for oil-based MWF distinction is 98.29 %. Comparing with results using the whole AE measurements, these results improve significantly. Good results show that the proposed data processing method could extract most useful information from the whole AE measurements in time domain for MWF distinction. Besides, the proposed method also provides an effective way for data analysis in the future.

The Effect of Azimuth Thruster on the Manoeuvring Ability of a Stern Trawler in Rough Weather Conditions

The results of computational analyses on the manoeuvring performance of a stern trawler with an azimuth thruster located in the forward part of the vessel are presented. The initial design of the vessel with a single shaft, propeller and rudder is considered to have insufficient manoeuvring performance with a trawl pull load of 40 tons in moderate environmental conditions with a forward speed of 3 knots. The modified design is equipped with an azimuth thruster located in the forward part of the vessel that can rotate 360 degrees to produce additional thrust. In order to assess and simulate the manoeuvring performance of the vessel, the surge, sway and yaw equations are set and solved in the time domain. The hydrodynamic forces due to surge and sway motions and the yaw moment are represented by a nonlinear modular mathematical model. The manoeuvring coefficients of the mathematical model employed are estimated by two distinct approaches; the first one is based on semi-empirical methods and the other is based on computational fluid dynamics (CFD). The external forces due to wind, current and waves are mathematically represented by proven semi-empirical methods based on the results of scaled model tests and full-scale measurements. A comprehensive computational test matrix is established and extensive computational analyses and manoeuvring simulations were carried out to indicate that an azimuth thruster with sufficient thrust located in the forward part of a stern trawler could significantly improve the manoeuvring performance of the vessel despite adverse environmental conditions.

Enhancement of Super Sort Sorting Algorithm Using Identical Value Search Concept

The Super Sort Sorting algorithm is a sorting algorithm that uses forward selection, backward selection, merging, and partition to sort a given list or data set. This study will focus on the enhancement of the Super Sort Sorting Algorithm on the forward selection part. This paper introduces an enhanced version of the algorithm where the concept of Identical Value Search was implemented on the first forward pass. This method eliminated the small sublists left behind by the original algorithm's forward pass and reduced the overall runtime of the algorithm.

A Dynamically Reconfigurable Autonomous Underwater Robot for Karst Exploration: Design and Experiment.

This paper presents the design and experiment of an autonomous underwater robot which can change the geometric configuration of its actuators, according to mission requirements or environmental constraints. The robot consists of two subsystems: forward part with three thrusters and backward part with four thrusters. The position and orientation of these thrusters can be dynamically changed during missions. Being different from most of other reconfigurable underwater robots which were designed as linked-modules, our robot has a unified design. It is suitable for specific mission in confined environments (e.g., karst exploration) in which the robot has to modify its shape to go through a narrow section or align the most part of its thrusters in the direction of a strong current, for examples. The design procedure, from hardware to software, of the robot is presented and experimental results are shown to demonstrate the versatility of the robot. Furthermore, the discussion and comparison between our robot and other underwater robots with adaptable actuation geometry are presented to highlight advantages of our design. Finally, the idea of using our robot for classic docking problem, which has some common features with karst exploration requirements in using dynamically reconfigurable robots, is discussed.

Development of Endoskeleton Type Knee Joint Assist Orthosis Using McKibben Type Artificial Muscle

The Japanese agriculture industry, faced with the problem of declining and aging farmers, has keenly called for the development of body assist orthoses for aiding agricultural workers. Therefore, in this study, we propose an endoskeleton-type knee joint assist orthosis for assisting knee joints in half-sitting postures or crouching postures. The proposed endoskeleton-type knee joint assist orthosis uses two McKibben-type artificial muscles per leg and four in total, placed from the waist to the foot along the forward part of the leg to generate assisting power. With the proposed orthosis in use, the contraction forces generated by the McKibben-type artificial muscles are converted into torque via the knees to assist the knee joints. This paper first presents the appearance of the prototyped endoskeleton-type knee joint assist orthosis, and then describes its components and the McKibben-type artificial muscles. To verify its actual effects on muscles, we conducted fitting experiments using a surface myoelectric electrometer to determine its assisting effects; these effects were found on certain experimental subjects, but there were no tangible effects on others. The experimental results suggest that the prototyped endoskeleton-type knee joint assist orthosis requires further improvement.

Towards a fault diagnosis method for rolling bearing with Bi-directional deep belief network

Intelligent fault diagnosis model based on machine learning algorithm has extensive application, while the unsatisfied training data quality leads to the lower accuracy of intelligent fault diagnosis and inferior generalization performance. To address this problem, a method called Bi-directional deep belief network (Bi-DBN) is developed for fault diagnosis of rolling bearings. The forward training part of Bi-DBN can learn the fault features from the original vibration signals first, and then the reverse generation part synthesizes samples according to the weight sharing by the forward training part. The noise time-shift layer is innovatively introduced to reduce the similarity between the synthesized sample and the original sample. Finally, Quantum genetic algorithm (QGA) is applied to optimize the key parameters of Bi-DBN to improve the feature learning efficiency. The experimental results of rolling bearing signals indicate that the developed method significantly limits the effect of training data quality on the diagnosis accuracy.