Field Penetration Research Articles

Optimized Random Forest Models for Rock Mass Classification in Tunnel Construction

The accurate prediction of rock mass quality ahead of the tunnel face is crucial for optimizing tunnel construction strategies, enhancing safety, and reducing geological risks. This study developed three hybrid models using random forest (RF) optimized by moth-flame optimization (MFO), gray wolf optimizer (GWO), and Bayesian optimization (BO) algorithms to classify the surrounding rock in real time during tunnel boring machine (TBM) operations. A dataset with 544 TBM tunneling samples included key parameters such as thrust force per cutter (TFC), revolutions per minute (RPM), penetration rate (PR), advance rate (AR), penetration per revolution (PRev), and field penetration index (FPI), with rock classification based on the Rock Mass Rating (RMR) method. To address the class imbalance, the Borderline Synthetic Minority Over-Sampling Technique was applied. Performance assessments revealed the MFO-RF model’s superior performance, with training and testing accuracies of 0.992 and 0.927, respectively, and key predictors identified as PR, AR, and RPM. Additional validation using 91 data sets confirmed the reliability of the MFO-RF model on unseen data, achieving an accuracy of 0.879. A graphical user interface was also developed, enabling field engineers and technicians to make instant and reliable rock classification predictions, greatly supporting safe tunnel construction and operational efficiency. These models contribute valuable tools for real-time, data-driven decision-making in tunneling projects.

Improved Boreability Index for Gripper TBMs in Medium- to Strong-Quality Rocks Based on Theoretical Analysis and Field Penetration Tests

Improved Boreability Index for Gripper TBMs in Medium- to Strong-Quality Rocks Based on Theoretical Analysis and Field Penetration Tests

Ion momentum mapping in photodissociation and Coulomb explosion events using ion time-of-flight analyzers

Abstract Modern light sources such as free electron lasers allow for tracking photoinduced events with an unprecedented accuracy. Ion spectroscopy is a particularly useful tool, revealing, e.g., momentum correlations in dissociation and Coulomb explosion patterns. Determining ion momentae and their relationship with the highest achievable accuracy is therefore very valuable. Here, we develop a systematic approach of accurate ion momentum determination in Wiley-McLaren type ion time-of-flight spectrometers taking into account also field penetration effects. The developed analytical formulae at various level of approximation are compared with ray tracing simulations and the effects are also illustrated on an experimental example.

Study of High Performance Nanoscale Channel Length Vertical Transistors with a Self-Aligned Blocking Layer.

A transistor design employing all vertically stacked components has attracted considerable attention due to the simplicity of the fabrication process and the high conductivity easily realized by achieving nanolevel short channel lengths with two-dimensional current paths. However, fundamental issues, specifically the blocking of the gate electrical field to the semiconductive channel layer and high leakage current at the "off" state, have impeded this configuration in becoming a major transistor design. To address these issues, it has been proposed to introduce a blocking layer (BL) with embedded hole structures and source electrode with embedded hole structures, enhancing gate field penetration and carrier modulation. The hole structure embedded in the source and the BL on the drain induced a desirable combined effect of gate field penetration and carrier pathway modulation. The align accuracy and the hole size difference between BL and source electrode were confirmed as the most important design parameters for high performance of a transistor. We therefore proposed a self-aligning lithography method using a built-in mask that allows high alignment accuracy between the source hole structure and the BL hole structure on the drain over a large area without a high-resolution process system. This method also enables easy and fast fabrication of nanoscale channels with high performance. This design resulted in a transistor with an output of 28 mA/cm2 and an on-off ratio exceeding 106 at 1 mV of VDS. However, at 3 V of VDS, the off-current increased significantly due to short-channel effects in the all metal electrode design. To solve this issue, Fermi level-tunable graphene replaced metal electrodes, maintaining an off-current below 10 pA and an on-off ratio around 107 at 3 V. In addition, the device demonstrates robust electrical properties to light without any special treatment and is stable with a threshold voltage shift of less than 1 V under bias stress. This study demonstrates that the proposed vertical transistor design is a viable candidate as a new major transistor design for various applications.

Geometric effects in the measurement of the remanent ferroelectric polarization at the nanoscale

A resurgence of research on ferroelectric materials has recently occurred due to their potential to enhance the performance of memory and logic. For the design and commercialization of such technologies, it is important to understand the physical behavior of ferroelectrics and the interplay with device size, geometry, and fabrication processes. Here, we report a study of geometric factors that can influence the measurement of the remanent ferroelectric polarization, an important measurement for understanding wakeup, retention, and endurance in ferroelectric technologies. The areal size scaling of W/Hf0.5Zr0.5O2/W capacitors is compared in two typical structures: an island top electrode with a continuous ferroelectric layer and an island top electrode/ferroelectric layer (etched ferroelectric layer). Error in the evaluation of the switched area leads to anomalous scaling trends and increasing apparent remanent polarization as capacitor sizes decrease, most strongly in continuous ferroelectric layer capacitors. Using TEM and electric field simulations, this is attributed to two effects: a processing artifact from ion milling that creates a foot on the top electrode and a fringe electric field penetrating outside of the capacitor area. With the correction of the switching area, the 2Pr for both samples agree (∼32 μC cm−2) and is invariant in the capacitor sizes used (down to 400 nm diameter). Our work demonstrates that the determination of the actual capacitor structure and local electric field is needed to evaluate the intrinsic ferroelectric behavior at the nanoscale.

Classification and prediction of rock mass drillability for a tunnel boring machine based on operational data mining

Currently, the accurate prediction of tunnel boring machine (TBM) performance remains a considerable challenge due to the complex interactions between the TBM and rock mass. In this study, the research work is based on part of a metro tunnel project that covers 2,083.94 m. The Gaussian mixture model (GMM) and K-nearest neighbor algorithm (KNN) are used to classify and predict the rock mass drillability in the TBM excavation process. Drillability indexes are introduced to cluster and classify the rock mass, including the penetration (P), field penetration index (FPI), torque penetration index (TPI), and specific energy (SE). Statistical characteristics of the drillability indexes were analyzed, and it was found that their distributions did not conform to the normal distribution, with large variation coefficients. Clustering analysis was then conducted on the TPI and FPI within the training group using the Gaussian mixture model, and six drillability categories of rock mass were classified. Subsequently, the mapping relationship between the cutterhead speed, advance speed, total advance force, and cutterhead torque in the training group and the drillability of rock mass was established based on the KNN classification model. It was revealed that when the K-value is set to 4, the model has high macro-F1, macro-P, and macro-R. Validated by the testing group data, this method has been proven to be feasible and effective. The research results indicate that this method can effectively classify and predict the drillability of tunneling surrounding rock mass in shield construction, particularly when the rock mass at the shield face is uniform and homogeneous. This provides a theoretical basis and technical support for safe and efficient shield tunneling.

Advanced predictive model and feature importance analysis for geological characteristics in tunnelling operations

Geological characteristics (GC) play a pivotal role in the efficiency and safety of tunnelling operations, necessitating accurate prediction methods. This study utilizes a dataset comprising operational parameters such as mean thrust (MT), penetration rate (PR), field penetration index (FPI), torque penetration index (TPI), advance rate (AR), mean cutterhead torque (MCT), and specific energy (SE). Bayesian optimization (BO) is employed for hyperparameter tuning of the extra trees (ET) algorithm to enhance prediction accuracy. The model was tasked with predicting three GCs using the operational parameters. The ET-BO model achieves near-perfect performance across all three GC classes, with an F1 score and accuracy of over 95%, during training. In testing, the model maintains strong performance with an F1 score and accuracy of over 91%, demonstrating robust predictive capability across all classes.

Research on magnetic field distribution characteristics of 2G-HTS dynamo in superconducting wireless power supply applications

Abstract The high-temperature superconducting (HTS) dynamo injects direct current (DC) into the winging of superconducting machines through non-electrical contact, solving issues such as thermal leakage in traditional current leads and current decay due to flux motion, joint resistance and AC losses. However, it has been observed that the DC output voltage decreases with an increasing air gap between the rotor magnet and HTS stator. To increase the output of the HTS dynamo at a fixed gap, this study employs an efficient numerical model based on the equivalent current method to investigate the magnetic field distribution of the magnets with different structural parameters. The relationship between the magnetic field distribution of the rotor magnet and the open-circuit voltage of the stator is established and extensively validated against simulation modeling and experimental data. Experimental results indicate that the rotor’s magnetic field distribution and the stator’s magnetic field penetration influence the open circuit voltage of the HTS dynamo. Specifically, when the distance between adjacent magnets is large, the magnetic field penetration occurs only on both sides of the stator, causing circuit voltage to increase initially and then decrease with the magnet distance decreases. A reverse point opposite to the magnetic field direction on both sides is generated at the center of the stator when the distance decreases further, which increases the average induced current density, and suppresses the downward trend. By optimizing the magnetic field distribution of the rotor magnets on the stator, the DC output power of the dynamo can be effectively improved. This model and the results contained in this article provide a comprehensive theoretical basis for researchers to compare and optimize their own modeling and experiment of the HTS dynamo.

Lower Ionospheric Disturbances Due To Geomagnetic Storms of March and April 2023: Inferred From VLF Navigational Signals and Numerical Simulations

AbstractD‐region effect of intense storm of 23 March (Dst = −163 nT) and super storm of 23 April 2023 (Dst = −212 nT) have been investigated using VTX and NWC transmitter signals recorded at low latitude station Dehradun (DDN; 30.31°N, 78.03°E), India. During March storm on 23 March, amplitude anomalies of +2.92 (NWC) and +1.91 dB (VTX) were observed. April storm showed amplitude anomalies of −9.41 (NWC) and −2.68 dB (VTX) on 23 April. Long Wave Propagation Capability code (LWPC) has been used to model signal anomalies to obtain D‐region Very low frequency (VLF) reference height (H′) and electron density gradient (β). During March storm, NWC‐Dehradun path showed a decrease in H′ by 12.04 km and an increase in β by 0.301 km−1, while VTX‐Dehradun path showed an increase in H′ by 0.07 km and β by 0.032 km−1 on 23 March. During April storm, VTX‐Dehradun path showed increase in H′ by 0.315 km and an increase in β by 0.024 km−1, while H′ increased by 2.91 km and β by 0.23 km−1 for NWC—Dehradun path on 23 April. Wavelet analysis of VLF anomalies showed signatures of atmospheric gravity waves of periods between 30 and 100 min during both storms associated with joule heating in the high/auroral latitudes. Results suggest that storm time prompt penetration (PP) of electric fields has contributed to VLF signal anomalies during April storm's main phase, which is also supported by PP Equatorial Electric Field Model run for the location of DDN station.

Applying Artificial Neural Networks with Bourgoyne and Young Model to Predict Rate of Penetration in Al-Garraf Oil Field

يعتبر توقع معدل الاختراق أمرا حاسما لعمليات حفر آبار النفط الآمنة والفعالة من حيث التكلفة. يمثل نموذج بورغوين ويانج نهجا قائما على المعادلات واسع الاستخدام تم تطويره في عام 1974 لتوقع معدل الاختراق في آبار النفط بناءً على الانحدار الخطي المتعدد. الشبكة العصبية الاصطناعية (ANN) هي تقنية تعلم آلي تقوم بتحليل البيانات وتقديم التوقعات. وقد تم إجراء العديد من الدراسات حول استخدام نموذج BYM على مستوى العالم، كما تم إجراء دراسات أخرى لتحسينه في ظروف مختلفة. الشبكات الصناعية الاصطناعية تستخدم أيضًا في هذه المجالات وقد حققت أداءً جيدًا. تقترح هذه الدراسة الاستفادة من مميزات كل من الشبكات العصبية التغذوية الأمامية من نموذج الشبكة العصبية الاصطناعية ومعادلات نموذج بورغوين ويونغ وتوظيفها في توقع معدل الاختراق. إن دمج معادلات نموذج بورغوين ويونغ مع الشبكة الصناعية الاصطناعية لتوقع معدل الاختراق يعني استخدام النموذج القائم على المعادلات إلى جانب قوة وقدرات التعلم الآلي لتحسين دقة وفعالية توقع معدل الاختراق في آبار النفط. يُظهر النهج المقترح ارتفاعًا في R2، وقلة في مجموع الاخطاء (البقايا) ، وقيمة P-value تساوي صفر، يشير هذا إلى أن النهج الحالي ذو قيمة كأداة مناسبة للتنبؤ الدقيق بمعدل الاختراق لخطط الحفر المستقبلية.

Inductance and penetration depth measurements of polycrystalline NbN films for all-NbN single flux quantum circuits

Abstract In this paper, we report on a systematic study of the inductance and magnetic field penetration depth (λ) of polycrystalline NbN superconducting thin films. By employing a four-metal-layer fabrication process specifically designed for all-NbN single-flux-quantum circuits, we constructed a superconducting quantum interference device loop composed of two parallel NbN SNS junctions, NbN microstrips, and NbN ground planes for precise inductance measurement. At 4.2 K, as the linewidth increases from 1 μm to 30 μm, the inductance per unit length (L u) of NbN microstrips significantly decreases, for example, the L u of 250 nm thick NbN microstrips drops from 0.907 pH/μm to 0.047 pH/μm. Compared to Nb, the L u of polycrystalline NbN microstrips is approximately two to three times that of Nb, offering an advantage for manufacturing smaller superconducting inductors. Furthermore, we conducted simulation analysis using InductEx software to extract the λ of NbN films of varying thicknesses. The results indicate that as the film thickness increased from 45 nm to 600 nm, λ initially decreased sharply and then stabilized, with values ranging from 430 nm to 323 nm. Notably, once the film thickness exceeded 200 nm, λ remained essentially constant, even at a temperature of 10 K, where it showed good stability, albeit with a slight increase (about 50 nm) compared to 4.2 K. This dependence of λ on thickness is reasonably explained by considering the effects of NbN film thickness on the superconducting critical temperature and residual resistivity. These research findings not only deepen our understanding of the characteristics of superconducting films but also lay a solid foundation for the future design and manufacture of more compact superconducting circuits at the higher temperature of 10 K.

Photochemical Reaction Front Propagation and Control in Molecular Crystals.

4-Fluoro-9-anthracenecarboxylic acid (4F-9AC) undergoes a reversible [4 + 4] photodimerization reaction that can generate bending and twisting in microcrystals. Larger crystals resist photomechanical deformation, permitting direct visualization of the [4 + 4] photodimerization reaction dynamics under a variety of conditions. Both birefringence and fluorescence imaging show that the photodimerization reaction starts preferentially at a crystal edge and then sweeps across the crystal as a propagating reaction front. To explain these results, a theory is developed that postulates an exponentially decaying mechanical reaction field that extends from product domain into reactant domains to catalyze the photochemical reaction. Analyzing the data with this theory, we estimate a reaction field penetration distance of ∼20 nm for the 4F-9AC crystal. The propagation velocity can be controlled by varying the excitation intensity or by embedding amorphous barrier regions into the crystal using electron beam lithography. These barriers can channel the reaction flow to select areas of the crystal while other regions remain unreacted.

Comparative Analysis of Thin and Thick MoTe2 Photodetectors: Implications for Next-Generation Optoelectronics.

Due to its outstanding optical and electronic properties, molybdenum ditelluride (MoTe2) has become a highly regarded material for next-generation optoelectronics. This study presents a comprehensive, comparative analysis of thin (8 nm) and thick (30 nm) MoTe2-based photodetectors to elucidate the impact of thickness on device performance. A few layers of MoTe2 were exfoliated on a silicon dioxide (SiO2) dielectric substrate, and electrical contacts were constructed via EBL and thermal evaporation. The thin MoTe2-based device presented a maximum photoresponsivity of 1.2 A/W and detectivity of 4.32 × 108 Jones, compared to 1.0 A/W and 3.6 × 108 Jones for the thick MoTe2 device at 520 nm. Moreover, at 1064 nm, the thick MoTe2 device outperformed the thin device with a responsivity of 8.8 A/W and specific detectivity of 3.19 × 109 Jones. Both devices demonstrated n-type behavior, with linear output curves representing decent ohmic contact amongst the MoTe2 and Au/Cr electrodes. The enhanced performance of the thin MoTe2 device at 520 nm is attributed to improved carrier dynamics resulting from effective electric field penetration. In comparison, the superior performance of the thick device at 1064 nm is due to sufficient absorption in the near-infrared range. These findings highlight the importance of thickness control in designing high-performance MoTe2-based photodetectors and position MoTe2 as a highly suitable material for next-generation optoelectronics.

Experimental study on the time-dependent spatial distribution of the three-dimensional magnetic field in an inductive pulsed plasma thruster

Inductive pulsed plasma thrust generates thrust by ionizing and accelerating plasma through pulsed inductive electromagnetic field. The spatial distribution of the magnetic field within the discharge region influences both the Lorentz force exerted on plasma and the electromagnetic coupling between plasma and circuit. An experimental prototype of inductive pulsed plasma thruster with high repeatability and a three-dimensional transient magnetic field measurement system with low integration error are established. Time-dependent spatial distribution of the magnetic field in the plasma is obtained by scanning measurement employing repeated pulse discharges. Combined with the results of high-speed photographing and electrical parameter measurements, the relationship between the evolution of plasma structure and the magnetic field penetration is discussed.

Ultra high sensitive long range surface plasmon resonance sensor for chemical and biochemical sensing

In this work, we proposed a long-range surface plasmon (LRSPR) sensor using a dielectric (Cytop) –Gold (Au)- graphene oxide (GO) layer to enhance the sensor’s detection accuracy (DA), figure of merit (FoM) and imaging sensitivity (Simag). The proposed sensor’s effectiveness for long-range is numerically demonstrated. The GO layer is used to protect the unwanted oxidation of Au and to improve the imaging sensitivity. By comparing its FoM and Simag to that of the traditional surface plasmon sensor (cSPR), the proposed sensor takes advantage of its high adsorption efficiency and high resolution. The Au-GO layer in the cSPR sensor and the cytop-Au-GO layer in the proposed sensor interfaces enhance the electric field intensity and penetration depth (PD). We attain the highest DA, FoM and Simag of 100/°, 1400/RIU, and 4183.9/RIU with 2000 nm of cytop thickness. For chemical and biological applications, the proposed LRSPR sensor design may be advantageous.

Ionospheric Disturbances Observed Over the Peruvian Sector During the Mother's Day Storm (G5‐Level) on 10–12 May 2024

AbstractThis article presents the recent extreme and rare G5‐level geomagnetic storm (Mother's Day Storm) effects on the equatorial and low‐latitude ionosphere observed at the Peruvian sector by the Jicamarca (11.9°S, 76.8°W, magnetic dip 1°N) incoherent scatter radar and associated instruments. This storm was produced by multiple Earth‐directed coronal mass ejections, which generated significant modifications in the Earth's magnetic field, leading to the Sym‐H of ∼−518 nT. On the dayside, due to the strong eastward penetration electric field, vertical plasma drift and equatorial electrojet (EEJ) enhanced for 2–3 hr and remained consistent at values of ∼95 m/s and 260 nT between 1700 and 1900 UT (1200 and 1400 LT). At the same time, vertical E B plasma drift uplifted the equatorial ionosphere, producing the dusk‐side super plasma fountain and transferring electron density to higher latitudes. A huge increase (∼1,325%) in electron density (from 11 to 142 TECu) is observed at low and mid‐latitudes from ∼20°S to 50°S between 2000 and 0400 UT (1500–2300 LT). The strong westward penetration electric field suppressed pre‐reversal enhancement, leading to downward plasma drift (∼−96 m/s) at around 2400 UT (1900 LT). Overnight, vertical plasma drift fluctuated between ±90 m/s, and the combined effect of penetration and disturbance dynamo electric fields caused a significant increase (∼530 km) in ionospheric virtual height. In the main and early recovery phase, consistent short‐ and long‐duration electric field disturbances persisted for approximately 30 hr, with periods of ∼48 and 90 min.

Ignition mechanism of a dual trigger electrode hollow cathode

Abstract Hollow cathodes serve as the electron source for thrusters and are critical elements of the electric propulsion system. To overcome the limitations of the small orifice blockage in the discharge path of the conventional hollow cathode on the ignition performance, a dual trigger electrode heater-based cathode with a sub-millimeter columnar bias electrode inserted into the emitter cavity is proposed. Both experiments and simulations indicate that the dual trigger electrode cathode provides faster ignition time and self-healing ability from poisoning. A coupled plasma-thermal model reveals the ignition mechanism from the discharge process, power deposition, and ion sputtering. The electric field penetration induced by the inner electrode enhances the energy injection of emitted electrons and internal ohmic heating to establish an efficient discharge from upstream to downstream. Primary ignition of the inner electrode enhances the power deposition on the emitter with ion bombardment dominated, raising the emitter temperature rapidly by 150 K from 1173 K in the preheating phase to accelerate the electrode switching. Energetic ions during the primary ignition perform explosive scanning sputtering of the emitter, uniformly cleaning the tungstate layer from the emitter surface and allowing the cathode to self-heal from poisoning, as verified by destructive environmental tests.

Analysis of azimuthal electron current driving by rotating magnetic field in field-reversed configuration electric propulsion

Field-reversed configuration electric propulsion is an advanced space electromagnetic propulsion technology with significant application prospects but poor thrust performance. To delve into the intrinsic physical mechanisms, a model of the rotating magnetic field penetrating into the plasma and azimuthal electron current driving was developed. The simulation results show that rotating magnetic field strength, frequency, and initial seed plasma density are the key factors that exist as an optimal threshold. Specifically, the rotating magnetic field feed current (i.e., magnetic field strength) was not less than 1000 A, the rotating magnetic field frequency was ∼200–300 kHz, and the plasma density was approximately 1 × 1018 m−3 order of magnitude.

TBM big data preprocessing method in machine learning and its application to tunneling

The big data generated by tunnel boring machines (TBMs) are widely used to reveal complex rock-machine interactions by machine learning (ML) algorithms. Data preprocessing plays a crucial role in improving ML accuracy. For this, a TBM big data preprocessing method in ML was proposed in the present study. It emphasized the accurate division of TBM tunneling cycle and the optimization method of feature extraction. Based on the data collected from a TBM water conveyance tunnel in China, its effectiveness was demonstrated by application in predicting TBM performance. Firstly, the Score-Kneedle (S-K) method was proposed to divide a TBM tunneling cycle into five phases. Conducted on 500 TBM tunneling cycles, the S-K method accurately divided all five phases in 458 cycles (accuracy of 91.6%), which is superior to the conventional duration division method (accuracy of 74.2%). Additionally, the S-K method accurately divided the stable phase in 493 cycles (accuracy of 98.6%), which is superior to two state-of-the-art division methods, namely the histogram discriminant method (accuracy of 94.6%) and the cumulative sum change point detection method (accuracy of 92.8%). Secondly, features were extracted from the divided phases. Specifically, TBM tunneling resistances were extracted from the free rotating phase and free advancing phase. The resistances were subtracted from the total forces to represent the true rock-fragmentation forces. The secant slope and the mean value were extracted as features of the increasing phase and stable phase, respectively. Finally, an ML model integrating a deep neural network and genetic algorithm (GA-DNN) was established to learn the preprocessed data. The GA-DNN used 6 secant slope features extracted from the increasing phase to predict the mean field penetration index (FPI) and torque penetration index (TPI) in the stable phase, guiding TBM drivers to make better decisions in advance. The results indicate that the proposed TBM big data preprocessing method can improve prediction accuracy significantly (improving R2s of TPI and FPI on the test dataset from 0.7716 to 0.9178 and from 0.7479 to 0.8842, respectively).

AC loss and shielding in stacks of coated conductors: Analytic modelling and comparison to experiment

This work explores the effects of the stacking of coated conductor tapes on their AC loss and penetration fields (Bp). The penetration field Bp and AC loss of short lengths of coated conducted tape stacks are analyzed, measured, and compared to a simple analytic model. The tape widths (w) and number of tapes in the stacks (Ns) were 4 mm (Ns = 1, 3, 5, series-305) and 12 mm (Ns = 1–40, series-244). Experimentally, the losses of the series-244 and series-305 tape stacks were measured in a spinning magnet calorimeter (SMC) in which the samples are exposed to a spinning field of frequency, f, up to 110 Hz and amplitude B0 = 566 mT and the power is measured by a calibrated boil-off calorimeter. These results were compared to calculated loss as a function of Ns for both tapes. In addition, the calculated Bp was plotted vs Ns. The basic effect observed was that stacking coated conductor tapes tended to form an effective composite with an increase Bp,comp and modified loss (For B0 < Bp,comp, Ptot was reduced with Ns, the effect saturating for Ns > 20). This could be understood in terms of treating a stack of conductors as a composite described in terms of a Brandt equations applied to a dilute superconductor. The results show and model the significantly reduced loss that stacks of conductors can have significantly for moderate levels of applied field. Such results are directly transferrable to cables of stacked strands.