Search

The pressure driven membrane-based air dehumidification technology is a promising energy-efficient technology for the air conditioning systems. To promote the engineering application of membrane dehumidification technology, factor significance analysis and multi-objective optimization of dehumidification membrane module were numerically investigated. Response surface method (RSM) was adopted to design the desired simulation cases and analyze the significance of five key factors, i.e., feed velocity, water vapor permeability coefficient, filling rate, fiber length, fiber diameter, on the membrane dehumidification characteristics. The second-order regression models of the dimensionless dehumidification amount per unit area (ma*) and the dehumidification rate (γ) were established based on the analysis of variance (ANOVA). It was found that the water vapor permeability coefficient has the most significant effect on γ and ma*. However, the dehumidification coefficient of performance (COP) and the frictional coefficient (f*Re) are hardly affected by the five independent factors. An average COP of 2.523 implies a good dehumidification efficiency of membrane module. As a multi-objective optimization method, the genetic algorithm was used for the membrane module optimization. The optimal solution represented by Pareto frontier is finally obtained in terms of γ and ma*. The optimized factor-level combination can be selected from the Pareto solution set according to the actual requirements of energy consumption and dehumidification efficiency.

The development of clean, stable, and renewable hot dry rock geothermal energy is significant for alleviating the energy crisis, reducing environmental pollution, and improving human health. Enhanced geothermal system (EGS) is an advanced technology for developing geothermal energy efficiently by stimulating hot dry rock reservoir. This technology involves a complex hydro-thermal coupling process. A numerical approach is usually applied for analyzing heat extraction. In this paper, taking the geothermal reservoir of the Cooper basin in Australia as the research object, two models–a 3D zonal homogeneous permeability model and a heterogenious permeability model – are established based on the measured microseismic data of hydraulic fracturing. The latter one is inversed from microseismic data. The temperature field, seepage field and thermal performance of the reservoir are numerically studied, and their differences are compared and analyzed. The results show that with the same injection-production flow rate, fluid flows more quickly from the injection well to the production well while the temperature drops relatively more rapidly in the inhomogeneous model due to the dominant channel revealed by dense microseismic events near the wellbore. In the homogeneous model, the dominant flow channel is not as pronounced as in the previous model, and the temperature decreases more slowly. During the operation of the geothermal reservoir model, the change in heat recovery of the zonal homogeneous model is relatively stable, with a decline of 3.74%, and that of the inhomogeneous model is rather obvious, with a decline of 12.72%. Compared with the inhomogeneous model, a smaller temperature drop and a higher heat recovery exist in the homogeneous model. However, the permeability in the actual reservoir is uneven, and the simulated heat recovery of the zonal homogenization model is higher than the actual recovery. Therefore, the simulation results of the inhomogeneous model have more reference significance for practical engineering.

In the era of big data, all‐optical control of the magnetization is recognized as an alternative scheme that boosts the accelerating advance of multifunctional integrated opto‐magnetization devices with high‐density capacity. The light‐induced magnetizations demonstrated so far are devoted to steering their spatial orientations and structures by engineering the complicated phase, amplitude, and polarization modulations of incident wavefronts, which, however, confront low efficiency, weak flexibility, and limited dimension. To tackle these issues efficaciously, a novel strategy is proposed to first achieve 5D opto‐magnetization composed of 3D spatial location, vectorial orientation as well as magnitude. This relies on physics‐enhanced deep learning incorporating multilayer perceptron (MLP) artificial neural network and opto‐magnetization principles. The preeminent magnetization morphology largely expedites the improvement in multi‐dimensional storage. The proposed facile approach is time‐efficient, flexible, and accurate to attain the prescribed magnetization. Moreover, the presenting findings and proposed route are not only applied for magnetization manipulation, but also applicable to the control of the structured light field.

In recent years, due to the rapid progress of urbanization, the subway system with the advantages of large transport capacity, punctuality, efficiency, convenience and safety has become one of the main transportation modes in metropolitan areas. With the increase in passenger flow, the comfort of subway passengers has attracted extensive attention from the academic community. In this paper, we begin by analyzing the characteristics of the subway environment and sort out six environmental elements that affect passengers’ comfort, including thermal environment, vibration, noise, lighting, air quality, and air pressure. In addition, the measurement scheme, calculation model, and evaluation method of each element are outlined based on relevant norms and literature. Through reviewing the studies in the past 2 decades, it is found that the in-depth research is still in demand for a comprehensive comfort evaluation model with multi-element coupling. A deep understanding of the subway passengers’ comfort is the basis for the design, development, and operation regulation of the subway environmental control system. Measures to improve comfort, especially the exploitation of energy-saving air conditioning systems, will provide strong support for the sustainable and sound growth of the rail transit industry.

Accurate gas saturation calculations are critical to evaluating the production of marine shale gas reservoirs. As a high-resolution exploration method, geophysical resistivity well-logging technology has been widely applied in almost all types of oil/gas reservoirs to evaluate formation fluid saturation. Although the calculated saturations are accurate for conventional reservoirs, it is a challenging task to acquire the gas saturation of shale gas reservoirs due to the presence of complex rock compositions and fluid types. It is necessary to analyze different influencing factors on electrical properties to establish a more applicable gas saturation model for marine shales. In this work, we make full use of geological data, well logging data, and rock experiment data to analyze the influencing factors of electrical properties in the Wufeng-Longmaxi Formation in the Sichuan Basin, China. Six conductive factors are studied, including stratigraphic structures, clay minerals, pyrite, organic matter, pore structures, and formation fluids. Then, a shale conductivity model is developed, in which high- and low-resistivity layers are connected in parallel. Based on the conductivity model, a new method for influencing factors of stepwise stripping conductivity is proposed to calculate shale gas saturation. Finally, by interpreting the well logging data of two shale gas wells, we compared the saturation calculation results of different methods to demonstrate the accuracy of the new method. The results show that thin, low-resistivity layers, clay minerals, pyrite and overmature carbonized organic matter reduce the resistivity of shale and weaken the contribution of fluids to the measured shale resistivity. Moreover, the calculation accuracy of this new method is better than that of Archie’s equation, Simandoux’s equation, and the neutron-density porosity overlay method. The findings of this paper will help gain insight into the mechanism of resistivity responses for marine shale reservoirs and improve the accuracy of the estimated gas saturation.

The nature of the measured data varies among different disciplines of geosciences. In rock engineering, features of data play a leading role in determining the feasible methods of its proper manipulation. The present study focuses on resolving one of the major deficiencies of conventional neural networks (NNs) in dealing with rock engineering data. Herein, since the samples are obtained from hundreds of meters below the surface with the utmost difficulty, the number of samples is always limited. Meanwhile, the experimental analysis of these samples may result in many repetitive values and 0s. However, conventional neural networks are incapable of making robust models in the presence of such data. On the other hand, these networks strongly depend on the initial weights and bias values for making reliable predictions. With this in mind, the current research introduces a novel kind of neural network processing framework for the geological that does not suffer from the limitations of the conventional NNs. The introduced single-data-based feature engineering network extracts all the information wrapped in every single data point without being affected by the other points. This method, being completely different from the conventional NNs, re-arranges all the basic elements of the neuron model into a new structure. Therefore, its mathematical calculations were performed from the very beginning. Moreover, the corresponding programming codes were developed in MATLAB and Python since they could not be found in any common programming software at the time being. This new kind of network was first evaluated through computer-based simulations of rock cracks in the 3DEC environment. After the model’s reliability was confirmed, it was adopted in two case studies for estimating respectively tensile strength and shear strength of real rock samples. These samples were coal core samples from the Southern Qinshui Basin of China, and gas hydrate-bearing sediment (GHBS) samples from the Nankai Trough of Japan. The coal samples used in the experiments underwent nuclear magnetic resonance (NMR) measurements, and Scanning Electron Microscopy (SEM) imaging to investigate their original micro and macro fractures. Once done with these experiments, measurement of the rock mechanical properties, including tensile strength, was performed using a rock mechanical test system. However, the shear strength of GHBS samples was acquired through triaxial and direct shear tests. According to the obtained result, the new network structure outperformed the conventional neural networks in both cases of simulation-based and case study estimations of the tensile and shear strength. Even though the proposed approach of the current study originally aimed at resolving the issue of having a limited dataset, its unique properties would also be applied to larger datasets from other subsurface measurements.

All‐optical control of magnetization vectors is considered as a powerful solution that empowers the development of multifunctional integrated optomagnetic devices. The interaction between vectorial light and magnetism has recently experienced a surge of interest due to its prominent ability to steer the magnetic polarization orientations and spatial textures within the subwavelength domain, which, however, usually requires complex wavefront coding optimizers or arduous material fabrication procedures. Herein, an optimization‐free all‐vectorial‐optical strategy for first realizing spatially twist‐controllable and successively polarization‐tunable magnetization at the subdiffraction scale through the inverse Faraday effect is conceived and demonstrated. This facile method relies on the coherent coupling of double crossed azimuthally polarized doughnut‐Gaussian vortex beams in a single optically configured geometry. It is found that both the magnetic twisting manifolds and polarization states, rotating from perpendicular (longitudinal), via hybrid (3D) to in‐plane (transverse) orientations, can be on‐demand mediated by judiciously tuning the crossing angles. It is further unraveled that the orbital angular momenta associated with topological charges of the vortex beams can be viewed as a vital knob to energetically maneuver magnetic twisting and guide magnetization switching. It is believed that the proposed route and presented findings are not only of great theoretical implication in twistronics and optomagnetic topology but also of considerable technological significance in multidimensional high‐density and low‐energy consumption optomagnetic storage and engineering of magnetic topologic materials.

Gas membrane separation technology is widely applied in different industry processes because of its advantages relating to separation performance and economic efficiency. It is usually difficult and time consuming to determine the suitable membrane materials for specific industrial separation processes through traditional experimental research methods. Molecular simulation is widely used to investigate the microscopic morphology and macroscopic properties of materials, and it guides the improvement of membrane materials. This paper comprehensively reviews the molecular-level exploration of the dominant mechanism and influencing factors of gas membrane-based separation. The thermodynamics and kinetics of polymer membrane synthesis, the molecular interactions among the penetrated gases, the relationships between the membrane properties and the transport characteristics of different gases in the composite membrane are summarized and discussed. The limitations and perspectives of the molecular simulation method in the study of the gas membrane separation process are also presented to rationalize its potential and innovative applications. This review provides a more comprehensive reference for promoting the materials’ design and engineering application of the gas separation membrane.

Detecting foot-ground contact is important for inertial motion capture systems because it can provide kinematic constraints to improve human motion capture accuracy. The popular contact detection methods based on zero-velocity detection are only applicable to applications with regular movement patterns, such as pedestrian dead reckoning and gait analysis. As for arbitrary locomotion in motion capture, reliable foot-ground contact detection for universal inertial motion capture is challenging in the presence of motion kinematics and dynamics estimation errors. This paper proposes a novel foot-ground contact detection method for inertial motion capture by the fusion of inaccurate estimations of body kinematics and dynamics with a Naive Bayes probabilistic contact model. Based on physical analysis of contact, a series of kinematic and dynamic motion features calculated from motion capture are considered as observations of contact status classification. The proposed framework consists of offline model training based on a constructed labeled multi-person foot contact dataset, real-time contact detection, and an online model adaptation mechanism for enhancing detection performance of different users and setups. Quantitative evaluations of the proposed contact detection method are presented, and the resulting average foot-ground contact detection accuracy among various locomotion is 95.8%.

Internet of Things (IoT) and Big Data technologies are becoming increasingly significant parts of national defense and the military, as well as in the civilian usage. The proper deployment of large-scale wireless sensor network (WSN) provides the foundation for these advanced technologies. Based on the Fruchterman–Reingold graph layout, we propose the Fruchterman–Reingold Hexagon (FR-HEX) algorithm for the deployment of WSNs. By allocating edges of hexagonal topology to sensor nodes, the network forms hexagonal network topology. A comprehensive evaluation of 50 simulations is conducted, which utilizes three evaluation metrics: average moving distance, pair correlation diversion (PCD), and system coverage rate. The FR-HEX algorithm performs consistently, the WSN topologies are properly regulated, the PCD values are below 0.05, and the WSN system coverage rate reaches 94%. Simulations involving obstacles and failed nodes are carried out to explore the practical applicability of the FR-HEX algorithm. In general, the FR-HEX algorithm can take full advantage of sensors’ hardware capabilities in the deployment. It may be a viable option for some IoT and Big Data applications in the near future.

Clay minerals significantly alter the pore size distribution (PSD) of the gas hydrate-bearing sediments and sandstone reservoir rock by adding an intense amount of micropores to the existing intragranular pore space. Therefore, in the present study, the internal pore space of various clay groups is investigated by manually segmenting Scanning Electron Microscopy (SEM) images. We focused on kaolinite, smectite, chlorite, and dissolution holes and characterized their specific pore space using fractal geometry theory and parameters such as pore count, pore size distribution, area, perimeter, circularity, and density. Herein, the fractal properties of different clay groups and dissolution holes were extracted using the box counting technique and were introduced for each group. It was observed that the presence of clays complicates the original PSD of the reservoir by adding about 1.31-61.30 pores/100 μm2 with sizes in the range of 0.003-87.69 μm2. Meanwhile, dissolution holes complicate the pore space by adding 4.88-8.17 extra pores/100 μm2 with sizes in the range of 0.06-119.75 μm2. The fractal dimension (D) and lacunarity (L) values of the clays’ internal pore structure fell in the ranges of 1.51-1.85 and 0.18-0.99, respectively. Likewise, D and L of the dissolution holes were in the ranges of, respectively, 1.63-1.65 and 0.56-0.62. The obtained results of the present study lay the foundation for developing improved fractal models of the reservoir properties which would help to better understand the fluid flow, irreducible fluid saturation, and capillary pressure. These issues are of significant importance for reservoir quality and calculating the accurate amount of producible oil and gas.

Core samples of the tight sandstone reservoir in the Bonan Oilfield were analyzed by using multiple petrophysical experimental techniques, then a multi-scale three-dimensional digital rock model was constructed. The pore structure parameters of tight sandstone and homogeneous Berea sandstone were compared. The electrical simulation method based on the digital rock model was utilized to quantitatively reveal the influence of five micro-pore structure parameters (pore size, throat size, pore-throat size, coordination number, and shape factor) on the rock’s electrical properties. In addition, the saturation of tight sandstone reservoirs was evaluated in combination with the three-component automatic mixed-connection conductivity model. The results show that the “non-Archie” phenomenon in sandstone is obvious, which is mainly caused by the small radius of the maximum connected pore throat and the complex structure of the pore throat. We noted that: with an increase in pore radius, throat radius, and coordination number, the formation factor decreases and tends to be stable; the pore-throat size increases and the formation factor decreases in the form of power function; the shape factor increases, and the formation factor increases; the larger the pore–throat ratio and shape factor, the greater the resistivity index; with an increase in coordination number, the resistivity index decreases; and the pore-throat size has no effect on the resistivity index. The calculation accuracy of oil saturation is improved by 6.54% by constructing the three-component automatic mixed-conductivity saturation model of tight sandstone.

The oil-based mud filtrate will invade the formation under the overbalanced pressure during drilling operations. As a result, alterations will occur to the nuclear magnetic resonance (NMR) response characteristics of the original formation, causing the relaxation time of the NMR T2 spectrum of the free fluid part to move towards a slower relaxation time. Consequently, the subsequent interpretation and petrophysical evaluation will be heavily impacted. Therefore, the actual measured T2 spectrum needs to be corrected for invasion. For this reason, considering the low-porosity and low-permeability of sandstone gas formations in the East China Sea as the research object, a new method to correct the incorrect shape of the NMR logging T2 spectrum was proposed in three main steps. First, the differences in the morphology of the NMR logging T2 spectrum between oil-based mud wells and water-based mud wells in adjacent wells were analyzed based on the NMR relaxation mechanism. Second, rocks were divided into four categories according to the pore structure, and the NMR logging T2 spectrum was extracted using the multidimensional matrix method to establish the T2 spectrum of water-based mud wells and oil-based mud wells. Finally, the correctness of the method was verified by two T2 spectrum correction examples of oil-based mud wells in the study area. The results show that the corrected NMR T2 spectrum eliminates the influence of oil-based mud filtrate and improves the accuracy of NMR logging for calculating permeability.

Although commercial motion-capture systems have been widely used in various applications, the complex setup limits their application scenarios for ordinary consumers. To overcome the drawbacks of wearability, human posture reconstruction based on a few wearable sensors have been actively studied in recent years. In this paper, we propose a deep-learning-based sparse inertial sensor human posture reconstruction method. This method uses bidirectional recurrent neural network (Bi-RNN) to build an a priori model from a large motion dataset to build human motion, thereby the low-dimensional motion measurements are mapped to whole-body posture. To improve the motion reconstruction performance for specific application scenarios, two fundamental problems in the model construction are investigated: training data selection and sparse sensor placement. The problem of deep-learning training data selection is to select independent and identically distributed (IID) data for a certain scenario from the accumulated imbalanced motion dataset with sufficient information. We formulate the data selection into an optimization problem to obtain continuous and IID data segments, which comply with a small reference dataset collected from the target scenario. A two-step heuristic algorithm is proposed to solve the data selection problem. On the other hand, the optimal sensor placement problem is studied to exploit most information from partial observation of human movement. A method for evaluating the motion information amount of any group of wearable inertial sensors based on mutual information is proposed, and a greedy searching method is adopted to obtain the approximate optimal sensor placement of a given sensor number, so that the maximum motion information and minimum redundancy is achieved. Finally, the human posture reconstruction performance is evaluated with different training data and sensor placement selection methods, and experimental results show that the proposed method takes advantages in both posture reconstruction accuracy and model training time. In the 6 sensors configuration, the posture reconstruction errors of our model for walking, running, and playing basketball are 7.25°, 8.84°, and 14.13°, respectively.

In this work, we present a feasible pathway for initially constructing light-induced isotropic super-resolved magnetization along with steerable orientations and trivial side-lobe levels. Toward this end, the counter-propagating azimuthally polarized vortex Hermite–Gauss beams are tailored with angular rotators at the exit pupil planes and then focused by using high numerical aperture objective lenses in a 4π optical microscopic configuration. By wilfully regulating the rotatable azimuth angle and judiciously optimizing the scaling parameter, the spherical super-resolved (λ3/24), orientation-tunable (any direction), and sidelobe-negligible (

Some inter-salt shale reservoirs have high oil saturations but the soluble salts in their complex lithology pose considerable challenges to their production. Low-field nuclear magnetic resonance (NMR) has been widely used in evaluating physical properties, fluid characteristics, and fluid saturation of conventional oil and gas reservoirs as well as common shale reservoirs. However, the fluid distribution analysis and fluid saturation calculations in inter-salt shale based on NMR results have not been investigated because of existing technical difficulties. Herein, to explore the fluid distribution patterns and movable oil saturation of the inter-salt shale, a specific experimental scheme was designed which is based on the joint adaptation of multi-state saturation, multi-temperature heating, and NMR measurements. This novel approach was applied to the inter-salt shale core samples from the Qianjiang Sag of the Jianghan Basin in China. The experiments were conducted using two sets of inter-salt shale samples, namely cylindrical and powder samples. Additionally, by comparing the one-dimensional (1D) and two-dimensional (2D) NMR results of these samples in oil-saturated and octamethylcyclotetrasiloxane-saturated states, the distributions of free movable oil and water were obtained. Meanwhile, the distributions of the free residual oil, adsorbed oil, and kerogen in the samples were obtained by comparing the 2D NMR T1-T2 maps of the original samples with the sample heated to five different temperatures of 80, 200, 350, 450, and 600 °C. This research puts forward a 2D NMR identification graph for fluid components in the inter-salt shale reservoirs. Our experimental scheme effectively solves the problems of fluid composition distribution and movable oil saturation calculation in the study area, which is of notable importance for subsequent exploration and production practices.

Seismic data and nuclear magnetic resonance (NMR) data are two of the highly trustable kinds of information in hydrocarbon reservoir engineering. Reservoir fluids influence the elastic wave velocity and also determine the NMR response of the reservoir. The current study investigates different pore types, i.e., micro, meso, and macropores’ contribution to the elastic wave velocity using the laboratory NMR and elastic experiments on coal core samples under different fluid saturations. Once a meaningful relationship was observed in the lab, the idea was applied in the field scale and the NMR transverse relaxation time (T2) curves were synthesized artificially. This task was done by dividing the area under the T2 curve into eight porosity bins and estimating each bin’s value from the seismic attributes using neural networks (NN). Moreover, the functionality of two statistical ensembles, i.e., Bag and LSBoost, was investigated as an alternative tool to conventional estimation techniques of the petrophysical characteristics; and the results were compared with those from a deep learning network. Herein, NMR permeability was used as the estimation target and porosity was used as a benchmark to assess the reliability of the models. The final results indicated that by using the incremental porosity under the T2 curve, this curve could be synthesized using the seismic attributes. The results also proved the functionality of the selected statistical ensembles as reliable tools in the petrophysical characterization of the hydrocarbon reservoirs.

Subsurface rocks exhibit multiscale heterogeneity characteristics ranging from the microscopic to macroscopic levels. A significant challenge in geophysical exploration research is how to accurately analyze the cross-scale characterization of rock component structures and physical responses. The advancement of rock imaging equipment and computational resources has led to the emergence of digital rock physics technology as a crucial tool for addressing these challenges. This paper explores common methods and issues in three dimensional modeling and numerical simulations, spanning from micro-nano scale rocks to meter-scale wellbores, and presents relevant research insights. An initial review of the previous research and evolving trends in multiscale rock modeling and physical property simulation is firstly carried out. Subsequently, the primary methods and application range of multiscale simulation are summarized, followed by an outline of the modeling approaches and application directions for digital wellbores. The progression from digital rocks to digital wellbores signifies the successful cross-scale application of digital rock physics technology from the microscopic to macroscopic levels. [ABSTRACT FROM AUTHOR]

The exploration and comprehensive assessment of fractured-vuggy reservoir information have perennially constituted focal points and challenges within the domain of oil and gas reservoir evaluation. The verification of geological phenomena, identification of various fracture and hole types, and the quantitative characterization thereof currently present pressing challenges. This study meticulously examines the deep carbonate reservoirs within the Dengying Formation in the Penglai gas region of the Sichuan Basin. The Core Rolling Scan images reveal five discernible features: unfilled holes, filled holes, filled fractures, open fractures, and algae. The analysis pinpoints three primary challenges in semantic segmentation recognition: the amalgamation of feature scales, class imbalance, and the scarcity of datasets with substantial sample sizes. To address these challenges, this paper introduces a Multi-Scale Feature Aggregation Pyramid Network model (MFAPNet), achieving a pixel accuracy of 68.04% in recognizing the aforementioned five types. Lastly, the model is employed in calculating core porosity, exposing a scaling relationship between wellbore image porosity and core porosity ranging from 1.5 to 3 times. To a certain extent, it reveals the correlation between the wellbore image logging data and the actual formation of the Dengying Formation in the Penglai Gas Field of the Sichuan Basin, and also provides a basis for the subsequent logging evaluation of the formation. The partial code and CHA355 dataset are publicly available at https://github.com/zyng886/MFAPNet. [ABSTRACT FROM AUTHOR]

Abstract It has been shown that the formation of filopodia is a key step in tumor cell metastasis, but there is limited research regarding its mechanism. In this study, we demonstrated that fatty acid synthase (FASN) promoted filopodia formation in liver cancer cells by regulating fascin actin-bundling protein 1 (FSCN1), a marker protein for filopodia. Mechanistically, on the one hand, the accumulation of FASN is caused by the enhanced deubiquitination of FASN mediated by UCHL5 (ubiquitin c-terminal hydrolase L5). In this pathway, low expression of SIAH1 (Seven in absentia homolog 1) can decrease the ubiquitination and degradation of ADRM1 (adhesion regulating molecule 1) thereby increasing its protein level, which will recruit and activate the deubiquitination enzyme UCHL5, leading to FASN undergo deubiquitination and escape from proteasomal degradation. On the other hand, the accumulation of FASN is related to its weakened ubiquitination, where SIAH1 directly acts as a ubiquitin ligase toward FASN, and low expression of SIAH1 reduces the ubiquitination and degradation of FASN. Both the two pathways are involved in the regulation of FASN in liver cancer. Our results reveal a novel mechanism for FASN accumulation due to the low expression of SIAH1 in human liver cancer and suggest an important role of FASN in filopodia formation in liver cancer cells.