Large Eddy Simulation of Vertical Structure and Size Density of Deep Layer Clouds

In a convective scheme featuring a discretized cloud size density, the assumed lateral mixing rate is inversely proportional to the exponential coefficient of plume size. This follows a typical assumption of-1, but it has unveiled inherent uncertainties, especially for deep layer clouds. Addressing this knowledge gap, we conducted comprehensive large eddy simulations and comparative analyses focused on terrestrial regions. Our investigation revealed that cloud formation adheres to the tenets of Bernoulli trials, illustrating power-law scaling that remains consistent regardless of the inherent deep layer cloud attributes existing between cloud size and the number of clouds. This scaling paradigm encompasses liquid, ice, and mixed phases in deep layer clouds. The exponent characterizing the interplay between cloud scale and number in the deep layer cloud, specifically for liquid, ice, or mixed-phase clouds, resembles that of shallow convection,but converges closely to zero. This convergence signifies a propensity for diminished cloud numbers and sizes within deep layer clouds. Notably, the infusion of abundant moisture and the release of latent heat by condensation within the lower atmospheric strata make substantial contributions. However, this role in ice phase formation is limited. The emergence of liquid and ice phases in deep layer clouds is facilitated by the latent heat and influenced by the wind shear inherent in the middle levels. These interrelationships hold potential applications in formulating parameterizations and post-processing model outcomes.

The role of polyurethane foam compressible layer in the mechanical behaviour of multi-layer yielding supports for deep soft rock tunnels

The polyurethane foam(PU)compressible layer is a viable solution to the problem of damage to the secondary lining in squeezing tunnels.Nevertheless,the mechanical behaviour of the multi-layer yielding supports has not been thoroughly investigated.To fill this gap,large-scale model tests were conducted in this study.The synergistic load-bearing mechanics were analyzed using the convergenceconfinement method.Two types of multi-layer yielding supports with different thicknesses(2.5 cm,3.75 cm and 5 cm)of PU compressible layers were investigated respectively.Digital image correlation(DIC)analysis and acoustic emission(AE)techniques were used for detecting the deformation fields and damage evolution of the multi-layer yielding supports in real-time.Results indicated that the loaddisplacement relationship of the multi-layer yielding supports could be divided into the crack initiation,crack propagation,strain-hardening,and failure stages.Compared with those of the stiff support,the toughness,deformability and ultimate load of the yielding supports were increased by an average of 225%,61%and 32%,respectively.Additionally,the PU compressible layer is positioned between two primary linings to allow the yielding support to have greater mechanical properties.The analysis of the synergistic bearing effect suggested that the thickness of PU compressible layer and its location significantly affect the mechanical properties of the yielding supports.The use of yielding supports with a compressible layer positioned between the primary and secondary linings is recommended to mitigate the effects of high geo-stress in squeezing tunnels.

Geological characteristics and models of fault-foldfracture body in deep tight sandstone of the second member of Upper Triassic Xujiahe Formation in Xinchang structural belt of Sichuan Basin,SW China

In the second member of the Upper Triassic Xujiahe Formation(T_(3)x_(2))in the Xinchang area,western Sichuan Basin,only a low percent of reserves has been recovered,and the geological model of gas reservoir sweet spot remains unclear.Based on a large number of core,field outcrop,test and logging-seismic data,the T_(3)x_(2) gas reservoir in the Xinchang area is examined.The concept of fault-fold-fracture body(FFFB)is proposed,and its types are recognized.The main factors controlling fracture development are identified,and the geological models of FFFB are established.FFFB refers to faults,folds and associated fractures reservoirs.According to the characteristics and genesis,FFFBs can be divided into three types:fault-fracture body,fold-fracture body,and fault-fold body.In the hanging wall of the fault,the closer to the fault,the more developed the effective fractures;the greater the fold amplitude and the closer to the fold hinge plane,the more developed the effective fractures.Two types of geological models of FFFB are established:fault-fold fracture,and matrix storage and permeability.The former can be divided into two subtypes:network fracture,and single structural fracture,and the later can be divided into three subtypes:bedding fracture,low permeability pore,and extremely low permeability pore.The process for evaluating favorable FFFB zones was formed to define favorable development targets and support the well deployment for purpose of high production.The study results provide a reference for the exploration and development of deep tight sandstone oil and gas reservoirs in China.

A gated recurrent unit model to predict Poisson’s ratio using deep learning

Static Poisson’s ratio(vs)is crucial for determining geomechanical properties in petroleum applications,namely sand production.Some models have been used to predict vs;however,the published models were limited to specific data ranges with an average absolute percentage relative error(AAPRE)of more than 10%.The published gated recurrent unit(GRU)models do not consider trend analysis to show physical behaviors.In this study,we aim to develop a GRU model using trend analysis and three inputs for predicting n s based on a broad range of data,n s(value of 0.1627-0.4492),bulk formation density(RHOB)(0.315-2.994 g/mL),compressional time(DTc)(44.43-186.9 μs/ft),and shear time(DTs)(72.9-341.2μ s/ft).The GRU model was evaluated using different approaches,including statistical error an-alyses.The GRU model showed the proper trends,and the model data ranges were wider than previous ones.The GRU model has the largest correlation coefficient(R)of 0.967 and the lowest AAPRE,average percent relative error(APRE),root mean square error(RMSE),and standard deviation(SD)of 3.228%,1.054%,4.389,and 0.013,respectively,compared to other models.The GRU model has a high accuracy for the different datasets:training,validation,testing,and the whole datasets with R and AAPRE values were 0.981 and 2.601%,0.966 and 3.274%,0.967 and 3.228%,and 0.977 and 2.861%,respectively.The group error analyses of all inputs show that the GRU model has less than 5% AAPRE for all input ranges,which is superior to other models that have different AAPRE values of more than 10% at various ranges of inputs.

A spatiotemporal deep learning method for excavation-induced wall deflections

Data-driven approaches such as neural networks are increasingly used for deep excavations due to the growing amount of available monitoring data in practical projects.However,most neural network models only use the data from a single monitoring point and neglect the spatial relationships between multiple monitoring points.Besides,most models lack flexibility in providing predictions for multiple days after monitoring activity.This study proposes a sequence-to-sequence(seq2seq)two-dimensional(2D)convolutional long short-term memory neural network(S2SCL2D)for predicting the spatiotemporal wall deflections induced by deep excavations.The model utilizes the data from all monitoring points on the entire wall and extracts spatiotemporal features from data by combining the 2D convolutional layers and long short-term memory(LSTM)layers.The S2SCL2D model achieves a long-term prediction of wall deflections through a recursive seq2seq structure.The excavation depth,which has a significant impact on wall deflections,is also considered using a feature fusion method.An excavation project in Hangzhou,China,is used to illustrate the proposed model.The results demonstrate that the S2SCL2D model has superior prediction accuracy and robustness than that of the LSTM and S2SCL1D(one-dimensional)models.The prediction model demonstrates a strong generalizability when applied to an adjacent excavation.Based on the long-term prediction results,practitioners can plan and allocate resources in advance to address the potential engineering issues.

Sand Cat Swarm Optimization with Deep Transfer Learning for Skin Cancer Classification

Skin cancer is one of the most dangerous cancer.Because of the high melanoma death rate,skin cancer is divided into non-melanoma and melanoma.The dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin lesions.Sometimes,pathology and biopsy examinations are required for cancer diagnosis.Earlier studies have formulated computer-based systems for detecting skin cancer from skin lesion images.With recent advancements in hardware and software technologies,deep learning(DL)has developed as a potential technique for feature learning.Therefore,this study develops a new sand cat swarm optimization with a deep transfer learning method for skin cancer detection and classification(SCSODTL-SCC)technique.The major intention of the SCSODTL-SCC model lies in the recognition and classification of different types of skin cancer on dermoscopic images.Primarily,Dull razor approach-related hair removal and median filtering-based noise elimination are performed.Moreover,the U2Net segmentation approach is employed for detecting infected lesion regions in dermoscopic images.Furthermore,the NASNetLarge-based feature extractor with a hybrid deep belief network(DBN)model is used for classification.Finally,the classification performance can be improved by the SCSO algorithm for the hyperparameter tuning process,showing the novelty of the work.The simulation values of the SCSODTL-SCC model are scrutinized on the benchmark skin lesion dataset.The comparative results assured that the SCSODTL-SCC model had shown maximum skin cancer classification performance in different measures.

Quantitative prediction model for the depth limit of oil accumulation in the deep carbonate rocks:A case study of Lower Ordovician in Tazhong area of Tarim Basin

With continuous hydrocarbon exploration extending to deeper basins,the deepest industrial oil accumulation was discovered below 8,200 m,revealing a new exploration field.Hence,the extent to which oil exploration can be extended,and the prediction of the depth limit of oil accumulation(DLOA),are issues that have attracted significant attention in petroleum geology.Since it is difficult to characterize the evolution of the physical properties of the marine carbonate reservoir with burial depth,and the deepest drilling still cannot reach the DLOA.Hence,the DLOA cannot be predicted by directly establishing the relationship between the ratio of drilling to the dry layer and the depth.In this study,by establishing the relationships between the porosity and the depth and dry layer ratio of the carbonate reservoir,the relationships between the depth and dry layer ratio were obtained collectively.The depth corresponding to a dry layer ratio of 100%is the DLOA.Based on this,a quantitative prediction model for the DLOA was finally built.The results indicate that the porosity of the carbonate reservoir,Lower Ordovician in Tazhong area of Tarim Basin,tends to decrease with burial depth,and manifests as an overall low porosity reservoir in deep layer.The critical porosity of the DLOA was 1.8%,which is the critical geological condition corresponding to a 100%dry layer ratio encountered in the reservoir.The depth of the DLOA was 9,000 m.This study provides a new method for DLOA prediction that is beneficial for a deeper understanding of oil accumulation,and is of great importance for scientific guidance on deep oil drilling.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.

Deep Learning for Wireless Physical Layer: Opportunities and Challenges

Machine learning(ML) has been widely applied to the upper layers of wireless communication systems for various purposes, such as deployment of cognitive radio and communication network. However, its application to the physical layer is hampered by sophisticated channel environments and limited learning ability of conventional ML algorithms. Deep learning(DL) has been recently applied for many fields, such as computer vision and natural language processing, given its expressive capacity and convenient optimization capability. The potential application of DL to the physical layer has also been increasingly recognized because of the new features for future communications, such as complex scenarios with unknown channel models, high speed and accurate processing requirements; these features challenge conventional communication theories. This paper presents a comprehensive overview of the emerging studies on DL-based physical layer processing, including leveraging DL to redesign a module of the conventional communication system(for modulation recognition, channel decoding, and detection) and replace the communication system with a radically new architecture based on an autoencoder. These DL-based methods show promising performance improvements but have certain limitations, such as lack of solid analytical tools and use of architectures that are specifically designed for communication and implementation research, thereby motivating future research in this field.

New evidence for the links between the local water cycle and the underground wet sand layer of a mega-dune in the Badain Jaran Desert, China

Scientists and the local government have great concerns about the climate change and water resources in the Badain Jaran Desert of western China. A field study for the local water cycle of a lake-desert system was conducted near the Noertu Lake in the Badain Jaran Desert from 21 June to 26 August 2008. An underground wet sand layer was observed at a depth of 20–50 cm through analysis of datasets collected during the field experiment. Measurements unveiled that the near surface air humidity increased in the nighttime. The sensible and latent heat fluxes were equivalent at a site about 50 m away from the Noertu Lake during the daytime, with mean values of 134.4 and 105.9 W/m2 respectively. The sensible heat flux was dominant at a site about 500 m away from the Noertu Lake, with a mean of 187.7 W/m2, and a mean latent heat flux of only 26.7 W/m2. There were no apparent differences for the land surface energy budget at the two sites during the night time. The latent heat flux was always negative with a mean value of –12.7 W/m2, and the sensible heat flux was either positive or negative with a mean value of 5.10 W/m2. A portion of the local precipitation was evaporated into the air and the top-layer of sand dried quickly after every rainfall event, while another portion seeped deep and was trapped by the underground wet sand layer, and supplied water for surface psammophyte growth. With an increase of air humidity and the occurrence of negative latent heat flux or water vapor condensation around the Noertu Lake during the nighttime, we postulated that the vapor was transported and condensed at the lakeward sand surface, and provided supplemental underground sand pore water. There were links between the local water cycle, underground wet sand layer, psammophyte growth and landscape evolution of the mega-dunes surrounding the lakes in the Badain Jaran Desert of western China.

Effect of slope gradient on the subsurface water flow velocity of sand layer profile

Subsurface water flow velocity influences the hydrodynamic characteristics of soil seepage and the interaction between subsurface water flow and surface runoff during soil erosion and sediment transport.A visualized method and equipment was adopted in this study to observe the subsurface water flow.Quartz sand was used as the test material of subsurface water flow and fluorescent dye was used as the indicator for tracing subsurface water flow.Water was supplied at the same flow discharge to the three parts at the bottom of the test flume,and the subsurface water flow were determined with four slope gradients(4°,8°,10°,and 12°).The results showed that the seepage velocity gradually increased with increasing slope gradient.The pore water velocity at different depths of sand layer profile increased with increasing slope gradient,whereas the thickness of the flow front gradually decreased.For the same slope gradient,the pore water velocity in the lower layer was the largest,whereas the thickness of the flow front was the smallest.Comparative analysis of the relationship between seepage velocity and pore water velocity at different depths of sand layer profile showed that the maximum relative difference between the measured pore water velocity and the computational pore water velocity at different depths of sand profile in the experiment was 4.38%.Thus,the test method for measuring the subsurface water flow velocity of sand layer profile adopted in this study was effective and feasible.The development of this experiment and the exploration of research methods would lay a good test foundation for future studies on the variation law of subsurface water flow velocity and the determination of flow velocity in purple soils,thus contributing to the improvement of the hydrodynamic mechanism of purple soils.

Interaction mechanism of the interface between a deep buried sand and a paleo-weathered rock mass using a high normal stress direct shear apparatus

In order to evaluate the feasibility of safe mining close to the contact zone under reduced security coal pillar conditions at a coal mine in eastern China, the interaction mechanism of the interface between deep buried sand and a paleo-weathered rock mass was investigated in the laboratory by direct shear testing. A DRS-1 high pressure soil shear testing machine and orthogonal design method were used in the direct shear tests. Variance and range methods were applied to analyze the sensitivity of each factor that has an influence on the mechanical characters of the interface. The test results show that the normal pressure is the main influencing factor for mechanical characteristics of the interface, while the lithological characters and roughness are minor factors; the shear stress against shear displacement curve for the interface shows an overall hyperbola relationship, no obvious peak stress and dilatancy was observed.When the normal pressure is 6 MPa, the shear strengths of interfaces with different roughness are basically the same, and when the normal pressure is more than 8 MPa, the larger the roughness of the interface, the larger will be the shear strength; the shear strength has a better linear relationship with the normal pressure, which can be described by a linear Mohr–Coulomb criterion.

Sedimentary Changes of a Sand Layer in Liquefied Silts

A flume experiment was conducted to investigate the restratification of liquefied sediment strata under a wave load with the focus on the interbedded strata of coarse and fine sediments formed in estuarine and coastal areas.The aim of this research was to study the characteristics and processes of liquefied sediment strata in terms of wave-induced liquefaction.In the experiment,the bottom bed liquefied under the wave action and the liquefied soil moved in the same period with the overlying waves,and the track of the soil particles in the liquefied soil was an ellipse.The sand layer consisting of coarse particles in the upper part,settled into the lower silt layer.The sinking of coarse particles and upward migration of the fine particles of the lower layer induced by liquefied sediment fluctuations are the likely reasons for sedimentation of the sand layer in liquefied silt.

Hydrodynamic deep drawing of double layered conical cups

Hydrodynamic deep drawing assisted by radial pressure is an advanced sheet forming technology with great advantages such as higher drawing ratio, good surface quality and higher dimensional accuracy. In this process, both the bottom surface and the peripheral edge of sheets are under hydrodynamic pressure, so that the forming procedure is more uniform with low failure probability. Multi-layered sheets with complex geometries could be formed more easily with this technique compared with other traditional methods. Rupture is the main irrecoverable failure form in sheet forming processes. Prediction of rupture occurrence is of great importance for determining and optimizing the proper process parameters. In this research, a theoretical model was proposed to calculate the critical rupture pressure in production of double layered conical parts with hydrodynamic deep drawing process assisted by radial pressure. The effects of other process parameters on critical rupture pressure, such as punch tip radius, drawing ratio, coefficient of friction, sheet thickness and material properties were also discussed. The proposed model was compared with finite element simulation and validated by experiments on Al1050/St13 double layered sheets, where a good agreement was found with analytical results.

Reduction of Electron Leakage in a Deep Ultraviolet Nitride Laser Diode with a Double-Tapered Electron Blocking Layer

A double-tapered AlGaN electron blocking layer (EBL) is proposed to apply in a deep ultraviolet semiconductor laser diode. Compared with the inverse double-tapered EBL, the laser with the double-tapered EBL shows a higher slope efficiency, which indicates that effective enhancement in the transportation of electrons and holes is achieved. Particularly, comparisons among the double-tapered EBL, the inverse double-tapered EBL, the singletapered EBL and the inverse single-tapered EBL show that the double-tapered EBL has the best performance in terms of current leakage.

Deep Learning Based Physical Layer Security of D2D Underlay Cellular Network

In order to improve the physical layer security of the device-to-device(D2D)cellular network,we propose a collaborative scheme for the transmit antenna selection and the optimal D2D pair establishment based on deep learning.Due to the mobility of users,using the current channel state information to select a transmit antenna or establish a D2D pair for the next time slot cannot ensure secure communication.Therefore,in this paper,we utilize the Echo State Network(ESN)to select the transmit antenna and the Long Short-Term Memory(LSTM)to establish the D2D pair.The simulation results show that the LSTMbased and ESN-based collaboration scheme can effectively improve the security capacity of the cellular network with D2D and increase the life of the base station.

Toward intelligent wireless communications:Deep learning-based physical layer technologies

Advanced technologies are required in future mobile wireless networks to support services with highly diverse requirements in terms of high data rate and reliability,low latency,and massive access.Deep Learning(DL),one of the most exciting developments in machine learning and big data,has recently shown great potential in the study of wireless communications.In this article,we provide a literature review on the applications of DL in the physical layer.First,we analyze the limitations of existing signal processing techniques in terms of model accuracy,global optimality,and computational scalability.Next,we provide a brief review of classical DL frameworks.Subsequently,we discuss recent DL-based physical layer technologies,including both DL-based signal processing modules and end-to-end systems.Deep neural networks are used to replace a single or several conventional functional modules,whereas the objective of the latter is to replace the entire transceiver structure.Lastly,we discuss the open issues and research directions of the DL-based physical layer in terms of model complexity,data quality,data representation,and algorithm reliability.

A deep trench super-junction LDMOS with double charge compensation layer

A deep trench super-junction LDMOS with double charge compensation layer(DC DT SJ LDMOS)is proposed in this paper.Due to the capacitance effect of the deep trench which is known as silicon-insulator-silicon(SIS)capacitance,the charge balance in the super-junction region of the conventional deep trench SJ LDMOS(Con.DT SJ LDMOS)device will be broken,resulting in breakdown voltage(BV)of the device drops.DC DT SJ LDMOS solves the SIS capacitance effect by adding a vertical variable doped charge compensation layer and a triangular charge compensation layer inside the Con.DT SJ LDMOS device.Therefore,the drift region reaches an ideal charge balance state again.The electric field is optimized by double charge compensation and gate field plate so that the breakdown voltage of the proposed device is improved sharply,meanwhile the enlarged on-current region reduces its specific on-resistance.The simulation results show that compared with the Con.DT SJ LD-MOS,the BV of the DC DT SJ LDMOS has been increased from 549.5 to 705.5 V,and the R_(on,sp) decreased to 23.7 mΩ·cm^(2).

Strength and dilatancy behaviors of deep sands in Shanghai with a focus on grain size and shape effect

With rapid development of infrastructures like tunnels and open excavations in Shanghai,investigations on deeper soils have become critically important.Most of the existing laboratory works were focused on the clayey strata up to Layer 6 in Shanghai,i.e.at depth of up to 40 m.In this paper,Layers 7,9,and 11,which were mostly formed of sandy soils at depth of up to 150 m,were experimentally investigated with respect to physico-mechanical behaviors.The stressestrain behaviors were analyzed by the consolidated drained/undrained(CD/CU)triaxial tests under monotonic loading.One-dimensional(1D)oedometer tests were performed to investigate the consolidation properties of the sandy soils.Specimens were prepared at three different relative densities for each layer.Also,the micro-images and particle size analyzers were used to analyze the shape and size of the sand grains.The influences of grain size,density,and angularity on the stressestrain behaviors and compressibility were also studied.Compared to the other layers,Layer 11 had the smallest mean grain size(D50),highest compressibility,and lowest shear strength.In contrast,Layer 9 had the largest mean grain size,lowest compressibility,and highest shear strength.Layer 7 was of intermediate mean grain size,exhibiting more compressibility and less shear strength than that of Layer 9.Also,the critical state parameters and maximum dilatancy rate of different layers were discussed.

Hydrocarbon test in lower-layer atmosphere to predict deep-sea petroleum or hydrate in the Okinawa Trough: an example

Light hydrocarbon (methane, ethane, propane, butane and CO2) test and C isotopic analysis of CO are conducted for over 100 lower-layer atmospheric samples from the East China Sea slope and the Okinawa Trough. The results show that the lower-layer atmosphere mainly consists of CO2 and then of CH4, and the CO2 concentrations are calculated to have a high average value of 0.87 omega/10(-2) about three times that of the regional background (0-3 omega/10(-2)). The result also shows that the average value of C isotope - 20.8 x 10(-3) is given to the CO2, inferring that it is inorganic gas. Thus, for the future 's work in the Okinawa Trough, special attention should be paid to CO2 hydrate, which is very possibly an important hydrate type.