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In this paper, a resolved CFD-DEM method based on the immersed boundary method is proposed to simulate the proppant transport process, which is a multi-phase problem with strong fluid-particle coupling mechanisms in the oil and gas industry. A multi-sphere model is integrated into this method to describe complex particle shapes, in which Lagrangian points uniformly distributed on the particle surface are efficiently utilized for solving particle-particle interactions. This approach is validated by several benchmarks, including single-sphere and two-sphere settling tests. A modified driving pressure gradient is also adopted to satisfy bulk velocity constraints for simulating particle settling problems in periodic channels. Transport and settling behaviors of hundreds of sphere and cylinder proppant particles in periodic narrow channels with different widths are investigated, and settling laws and apparent viscosity models for proppant clouds with different shapes are then extracted from the simulation results. Benefiting from the features of multi-sphere modeling, this approach is demonstrated to be both robust and efficient for simulating fluid-particle coupling flow with complex particle shapes.

An efficient solver for the pseudo-2-dimensional (P2D) model of Li-ion batteries is developed based on the open-source computational fluid dynamics platform OpenFOAM. Species and charge conservation equations are solved in a segregated manner: potential and concentration of electrolyte and solid electrode are solved sequentially. The non-linearity and interlinkage are handled by an iterative procedure. In the P2D model, solving Li-ion (de)intercalation in solid particles implicitly is time-consuming. The Picard method is employed for calculating solid concentration explicitly without the need to solve the discretized equations system. In one time step, Li-ion concentration of future time is recursively approximated until convergence. The explicit solving of solid concentration in combination with the overall iterative procedure makes solving of the P2D model efficient. Nonlinear source terms are linearized whenever possible to retain stability. Adaptive time-stepping is devised, grounded on the derivative of open circuit voltage (OCV) with regard to Li-ion concentration at the particle surface. The developed solver is validated in detail with respect to COMSOL. Performance under various operation conditions, such as discharge of rate 10 C, hybrid charge-discharge cycle, and urban dynamometer driving schedule (UDDS), demonstrates its robustness.

The datalink of an unmanned aerial vehicle (UAV) is vulnerable to external electromagnetic interference (EMI). The characteristic parameters of the EMI signal can be definitively detected by a developed monitoring platform mounted on the UAV, but it is impossible to determine the lost-link thresholds of the datalink in any state by experiment because the state of the datalink varies dynamically with the flight distance and the UAV's attitudes. In this case, whether a certain state of the UAV's datalink will be interrupted or not by the EMI cannot be evaluated. In this article, a method of EMI prediction for the UAV's dynamic datalink based on the Gaussian process regression (GPR) is proposed, two indicators, including the interfering frequency and the operation signal power of the datalink, are taken as the training inputs, and the lost-link thresholds of the datalink obtained by the EMI injection test are taken as the training targets. The results show that this method can effectively fit the training samples and determine a 95% confidence interval of the predictive outputs, which has a low predictive error, less than 2.2 dB. Compared with the methods of the layer recurrent neural network and support vector machine, the GPR method has higher predictive accuracy and a stronger generalization ability. Therefore, the detected EMI characteristic parameters can be used as new input samples to predict the lost-link thresholds in any condition. On this basis, the EMI margin can be calculated to evaluate the degree of the anti-EMI redundancy of the UAV's datalink, which can be used for the active adaptation of the UAV's datalink to EMI in the future.

所有实验数据都包含在 Excel 文件中。使用 CMG-STARS 进行模拟。此处给出了其中一种场景的示例输入文件。用于实验云图、浓度分布和复杂电位计算的Matlab 和 Python 代码显示在相应的 zip 文件中。

The Li-ion battery is a complex physicochemical system that generally takes applied current as input and terminal voltage as output. The mappings from current to voltage can be described by several kinds of models, such as accurate but inefficient physics-based models, and efficient but sometimes inaccurate equivalent circuit and black-box models. To realize accuracy and efficiency simultaneously in battery modeling, we propose to build a data-driven surrogate for a battery system while incorporating the underlying physics as constraints. In this work, we innovatively treat the functional mapping from current curve to terminal voltage as a composite of operators, which is approximated by the powerful deep operator network (DeepONet). Its learning capability is firstly verified through a predictive test for Li-ion concentration at two electrodes. In this experiment, the physics-informed DeepONet is found to be more robust than the purely data-driven DeepONet, especially in temporal extrapolation scenarios. A composite surrogate is then constructed for mapping current curve and solid diffusivity to terminal voltage with three operator networks, in which two parallel physics-informed DeepONets are firstly used to predict Li-ion concentration at two electrodes, and then based on their surface values, a DeepONet is built to give terminal voltage predictions. Since the surrogate is differentiable anywhere, it is endowed with the ability to learn from data directly, which was validated by using terminal voltage measurements to estimate input parameters. The proposed surrogate built upon operator networks possesses great potential to be applied in on-board scenarios, such as battery management system, since it integrates efficiency and accuracy by incorporating underlying physics, and also leaves an interface for model refinement through a totally differentiable model structure., 32 pages, 15 figures

To improve future tropical cyclone forecasts, researchers sent a remotely operated saildrone into the extreme winds and towering waves around the eye of a category 4 hurricane.

Permeability is a key parameter for characterizing fluid flow in digital rocks. It depends on pore geometry and topology and can be computed numerically via solving the Stokes equation. However, the associated computational effort can be enormous for large-scale models, even using the efficient Lattice-Boltzmann method. In this study, an efficient method is developed for computing the equivalent permeability of digital rocks by simplifying the Stokes equation to Darcy equation. The method is based on the idea that a 3D digital core can be approximated by the combination of multiple 2D slices/layers, and the property of each layer is governed by the Stokes equation. Specifically, to mimic the 2D fine-scale velocity solved from the Stokes equation, a local permeability is assigned according to the velocity for each voxel. In addition, the nearest distance from each voxel to the solid wall is used to approximate the 2D fine-scale velocity, without the need of solving the Stokes equation in 2D for each layer. By this means, the 3D Stokes equation can be simplified to multiple 2D cases that provide the local permeability distribution for the 3D Darcy equation, and thus the computational cost can be significantly reduced. Case studies have been conducted on various samples in different scales. The results demonstrate that the computed 3D permeabilities using finite difference method (based on the Darcy equation) agree well with those using Lattice-Boltzmann method (based on the Stokes equation), and a speedup factor of about O(10) is achieved. The method can be applied to both sandstone and carbonate rocks for fast estimation of block permeability.

Observations from uncrewed surface vehicles (saildrones) in the Bering, Chukchi, and Beaufort Seas during June – September 2019 were used to evaluate initial conditions and forecasts with lead times up to 10 days produced by eight operational numerical weather prediction centers. Prediction error behaviors in pressure and wind are found to be different from those in temperature and humidity. For example, errors in surface pressure were small in short-range (

The deep ocean is severely undersampled. Whereas shipboard measurements provide irregular spatial and temporal records, moored records establish deep ocean high-resolution time series, but only at limited locations. Here, highlights and challenges of measuring abyssal temperature and salinity on the Kuroshio Extension Observatory (KEO) mooring (32.3°N, 144.6°E) from 2013 to 2019 are described. Using alternating SeaBird 37-SMP instruments on annual deployments, an apparent fresh drift of 0.03–0.06 psu was observed, with each newly deployed sensor returning to historical norms near 34.685 psu. Recurrent salinity discontinuities were pronounced between the termination of each deployment and the initiation of the next, yet consistent pre- and postdeployment calibrations suggested the freshening was “real.” Because abyssal salinities do not vary by 0.03–0.06 psu between deployment locations, the contradictory salinities during mooring overlap pointed toward a sensor issue that self-corrects prior to postcalibration. A persistent nepheloid layer, unique to KEO and characterized by murky, sediment-filled water, is likely responsible for sediment accretion in the conductivity cell. As sediment (or biofouling) increasingly clogs the instrument, salinity drifts toward a fresh bias. During ascent, the cell is flushed, clearing the clogged instrument. In contrast to salinity, deep ocean temperatures appear to increase from 2013 to 2017 by 0.0059°C, whereas a comparison with historical deep temperature measurements does not support a secular temperature increase in the region. It is suggested that decadal or interannual variability associated with the Kuroshio Extension may have an imprint on deep temperatures. Recommendations are discussed for future abyssal temperature and salinity measurements.

Geomechanical logs are of ultimate importance for subsurface description and evaluation, as well as for the exploration of underground resources, such as oil and gas, groundwater, minerals, and geothermal energy. Together with geological and hydrological properties, low-cost and high-accuracy models can be generated based on geomechanical parameters. However, it is challenging to directly measure geomechanical parameters, and they are usually estimated based on other measured quantities. For example, geomechanical logs may be obtained with certain empirical models from sonic logs together with prior information such as rock types, which are not readily available. Finding a way to directly estimate geomechanical logs based on easily available conventional well logs can result in significant cost savings and increased efficiency. In this article, we showed that deep learning via the long short-term memory network (LSTM) is effective in constructing an end-to-end model that takes the spatial dependence in well logs into consideration. We further proposed a physics-constrained LSTM, in which the physical mechanism behind the geomechanical parameters is utilized as a priori information. This state-of-the-art model is capable to directly estimate geomechanical logs based on easily available data, and it achieves higher prediction accuracy since the domain knowledge of the problem is considered.

SummaryIn the development of fractured reservoirs, geomechanics is crucial because of the stress sensitivity of fractures. However, the complexities of both fracture geometry and fracture mechanics make it challenging to consider geomechanical effects thoroughly and efficiently in reservoir simulations. In this work, we present a coupled geomechanics and multiphase-multicomponent flow model for fractured reservoir simulations. It models the solid deformation using a poroelastic equation, and the solid deformation effects are incorporated into the flow model rigorously. The noticeable features of the proposed model are it uses a pseudocontinuum equivalence method to model the mechanical characteristics of fractures; the coupled geomechanics and flow equations are split and sequentially solved using the fixed-stress splitting strategy, which retains implicitness and exhibits good stability; and it simulates geomechanics and compositional flow, respectively, using a dual-grid system (i.e., the geomechanics grid and the reservoir-flow grid). Because of the separation of the geomechanics part and the flow part, the model is not difficult to implement based on an existing reservoir simulator. We validated the accuracy and stability of this model through several benchmark cases and highlighted the practicability with two large-scale cases. The case studies demonstrate that this model is capable of considering the key effects of geomechanics in fractured-reservoir simulation, including matrix compaction, fracture normal deformation, and shear dilation, as well as hydrocarbon phase behavior. The flexibility, efficiency, and comprehensiveness of this model enable a more realistic geocoupled reservoir simulation.

Unmanned aerial vehicle (UAV) is likely to be interfered by high-power microwave (HPM) pulse, especially its datalink. To investigate the effects of HPM pulse on the datalink of a UAV in a dynamic flight scenario, a tracking interference model for an HPM source toward a UAV is proposed, as well a dynamic flight model is also built to determine the HPM reception of the UAV's datalink. In order to predict the possible effects of HPM on the datalink, the tests on a certain type of UAV's datalink are carried out in the presence of HPM with its carrier frequency in L -, S - and C -band, respectively. The results show that the electromagnetic interference (EMI) can easily enter the receiver via front-door when the carrier frequency of the HPM falls into the operating frequency band of the datalink, which will lead to a permanent physical damage. However, when the carrier frequency deviates from the operating frequency band of the datalink, the EMI couples into the incomplete shielded power line via back-door and generates a high-voltage signal at the power port, resulting in a transient lost-link effect or even a functional failure of the datalink due to its abnormal electric power supply. Finally, some suggestions are put forward to improve the UAV's adaptability in the HPM environment.

This study proposes a supervised learning method that does not rely on labels. We use variables associated with the label as indirect labels, and construct an indirect physics-constrained loss based on the physical mechanism to train the model. In the training process, the model prediction is mapped to the space of value that conforms to the physical mechanism through the projection matrix, and then the model is trained based on the indirect labels. The final prediction result of the model conforms to the physical mechanism between indirect label and label, and also meets the constraints of the indirect label. The present study also develops projection matrix normalization and prediction covariance analysis to ensure that the model can be fully trained. Finally, the effect of the physics-constrained indirect supervised learning is verified based on a well log generation problem., 5 pages and 5 figures

The ocean surface albedo is responsible for the distribution of solar (shortwave) radiant energy between the atmosphere and ocean and therefore is a key parameter in Earth’s surface energy budget. In situ ocean observations typically do not measure upward reflected solar radiation, which is necessary to compute net solar radiation into the ocean. Instead, the upward component is computed from the measured downward component using an albedo estimate. At two NOAA Ocean Climate Station buoy sites in the North Pacific, the International Satellite Cloud Climatology Project (ISCCP) monthly climatological albedo has been used, while for the NOAA Global Tropical Buoy Array a constant albedo is used. This constant albedo is also used in the Coupled Ocean–Atmosphere Response Experiment (COARE) bulk flux algorithm. This study considers the impacts of using the more recently available NASA Cloud and the Earth’s Radiant Energy System (CERES) albedo product for these ocean surface heat flux products. Differences between albedo estimates in global satellite products like these imply uncertainty in the net surface solar radiation heat flux estimates that locally exceed the target uncertainty of 1.0 W m−2 for the global mean, set by the Global Climate Observing System (GCOS) of the World Meteorological Organization (WMO). Albedo has large spatiotemporal variability on hourly, monthly, and interannual time scales. Biases in high-resolution SWnet (the difference between surface downwelling and upwelling shortwave radiation) can arise if the albedo diurnal cycle is unresolved. As a result, for periods when satellite albedo data are not available it is recommended that an hourly climatology be used when computing high-resolution net surface shortwave radiation.

An important and incompletely answered question is whether machine learning methods can be used to discover the excitation of rogue waves (RWs) in nonlinear systems, especially their dynamic properties and phase transitions. In this work, a theory-guided neural network (TgNN) is constructed to explore the RWs of one-dimensional Bose-Einstein condensates. We find that such method is superior to the ordinary deep neural network due to theory guidance of underlying problems. The former can directly give any excited location, timing, and structure of RWs using only a small amount of dynamic evolution data as the training data, without the tedious step-by-step iterative calculation process. In addition, based on the TgNN approach, a phase transition boundary is also discovered, which clearly distinguishes the first-order RW phase from the non-RW phase. The results not only greatly reduce computational time for exploring RWs, but also provide a promising technique for discovering phase transitions in parameterized nonlinear systems.

Summary This paper focuses on the numerical simulation of particle (“proppant”) transport in channel fracturing, in which fiber-proppant fluid is pumped intermittently into hydraulic fractures, alternated with clean fluid pulses. The pulsed pumping protocol leads to heterogeneous/channelized proppant distribution in fractures, generating open flow channels with high conductivity. To understand the evolution of the channelized proppant distribution, we develop an efficient pseudo-3D multiphase particle-in-cell (P3D MP-PIC) method to simulate the proppant transport during the pumping process. Compared with the Eulerian-Eulerian (EE) models, the MP-PIC approach has higher accuracy in modeling particle-fluid and particle-particle interactions by tracing the particles in a Lagrangian fashion. Compared with the computational fluid dynamics-discrete element method (CFD-DEM), the MP-PIC method is more computationally efficient due to a parcel feature and the fast calculation of particle forces. Reduced from the 3D MP-PIC method, the P3D MP-PIC method has better computational efficiency and flexibility to couple with other subsurface processes (e.g., fracture propagation and fluid leakoff) while also achieving sufficient accuracy for engineering purposes. Due to the Eulerian-Lagrangian (EL) nature, the P3D MP-PIC method can track the trajectories of the fiber-proppant clusters whose effective viscosity and the settling velocity are determined based on the laboratory results. With an accurate description of fluid and particle dynamics, this work reveals the critical physical mechanisms of the proppant transport in channel fracturing: stable displacement and Saffman-Taylor (ST) instability. The alternate occurrence of these two mechanisms gives rise to the channelized proppant distribution. To investigate the influential factors of the channel patterns, we conduct parametric studies on the operational parameters, including injection rate, fiber concentration, and pulsing mode. The effect of fracture propagation and gravitational settling is also discussed in detail.

Imposing physical constraints on neural networks as a method of knowledge embedding has achieved great progress in solving physical problems described by governing equations. However, for many engineering problems, governing equations often have complex forms, including complex partial derivatives or stochastic physical fields, which results in significant inconveniences from the perspective of implementation. In this paper, a scientific machine learning framework, called AutoKE, is proposed, and a reservoir flow problem is taken as an instance to demonstrate that this framework can effectively automate the process of embedding physical knowledge. In AutoKE, an emulator comprised of deep neural networks (DNNs) is built for predicting the physical variables of interest. An arbitrarily complex equation can be parsed and automatically converted into a computational graph through the equation parser module, and the fitness of the emulator to the governing equation is evaluated via automatic differentiation. Furthermore, the fixed weights in the loss function are substituted with adaptive weights by incorporating the Lagrangian dual method. Neural architecture search (NAS) is also introduced into the AutoKE to select an optimal network architecture of the emulator according to the specific problem. Finally, we apply transfer learning to enhance the scalability of the emulator. In experiments, the framework is verified by a series of physical problems in which it can automatically embed physical knowledge into an emulator without heavy hand-coding. The results demonstrate that the emulator can not only make accurate predictions, but also be applied to similar problems with high efficiency via transfer learning.

Data-driven discovery of partial differential equations (PDEs) has recently made tremendous progress, and many canonical PDEs have been discovered successfully for proof of concept. However, determining the most proper PDE without prior references remains challenging in terms of practical applications. In this work, a physics-informed information criterion (PIC) is proposed to measure the parsimony and precision of the discovered PDE synthetically. The proposed PIC achieves satisfactory robustness to highly noisy and sparse data on 7 canonical PDEs from different physical scenes, which confirms its ability to handle difficult situations. The PIC is also employed to discover unrevealed macroscale governing equations from microscopic simulation data in an actual physical scene. The results show that the discovered macroscale PDE is precise and parsimonious and satisfies underlying symmetries, which facilitates understanding and simulation of the physical process. The proposition of the PIC enables practical applications of PDE discovery in discovering unrevealed governing equations in broader physical scenes.

The enantioseparation of chiral molecules is a crucial and challenging task in the field of experimental chemistry, often requiring extensive trial and error with different experimental settings. To overcome this challenge, here we show a research framework that employs machine learning techniques to predict retention times of enantiomers and facilitate chromatographic enantioseparation. A documentary dataset of chiral molecular retention times in high-performance liquid chromatography (CMRT dataset) is established to handle the challenge of data acquisition. A quantile geometry-enhanced graph neural network is proposed to learn the molecular structure-retention time relationship, which shows a satisfactory predictive ability for enantiomers. The domain knowledge of chromatography is incorporated into the machine learning model to achieve multi-column prediction, which paves the way for chromatographic enantioseparation prediction by calculating the separation probability. The proposed research framework works well in retention time prediction and chromatographic enantioseparation facilitation, which sheds light on the application of machine learning techniques to the experimental scene and improves the efficiency of experimenters to speed up scientific discovery.

As an essential attribute of organic compounds, polarity has a profound influence on many molecular properties such as solubility and phase transition temperature. Thin layer chromatography (TLC) represents a commonly used technique for polarity measurement. However, current TLC analysis presents several problems, including the need for a large number of attempts to obtain suitable conditions, as well as irreproducibility due to non-standardization. Herein, we describe an automated experiment system for TLC analysis. This system is designed to conduct TLC analysis automatically, facilitating high-throughput experimentation by collecting large experimental datasets under standardized conditions. Using these datasets, machine learning (ML) methods are employed to construct surrogate models correlating organic compounds’ structures and their polarity using retardation factor (Rf). The trained ML models are able to predict the Rf value curve of organic compounds with high accuracy. Furthermore, the constitutive relationship between the compound and its polarity can also be discovered through these modeling methods, and the underlying mechanism is rationalized through adsorption theories. The trained ML models not only reduce the need for empirical optimization currently required for TLC analysis, but also provide general guidelines for the selection of conditions, making TLC an easily accessible tool for the broader scientific community.

Diverting agents that could plug a targeted location and fill different shapes of fractures have attracted an increasing attention in the field of fracturing. In this study, a series of novel diverting agents were prepared by mixing aldehyde sodium alginate (ASA) and amino gelatin (AG) through a Schiff base reaction to form a primary network, polylactic acid (PLA) fibers were added to produce a 3D network, and Ca2+ was mixed in as an ionic cross‐linking agent to strengthen the cross‐linking points. It was found that gelation time, swelling behavior, and mechanical performance could be controlled by varying the content of aldehyde groups and amino groups. Gelation time could be controlled in 1‐10 minutes. Plugging behavior and degradation tests demonstrated that the hydrogel could suit fractures with various shapes and plug effectively under high temperatures and pressures, and it could eventually become acidic fluid to help clean the wellbore. This novel ASA/AG/PLA hydrogel constitutes a promising environmentally friendly diverting agent in the application of diversion fracturing.

Solid materials such as rocks can contain primary defects, and internal defects are activated in the event of mining disturbance, which causes rock damage and destruction. Therefore, it is of great significance for rock engineering to study the mechanical properties and failure mechanism of damaged rock. In this study, damaged prefabricated crack sandstone specimens were prepared with the cyclic loading-unloading test, and the uniaxial loading test was carried out with damaged specimens. The evolution law of peak strength, elastic modulus, and peak strain of specimens with different damage degrees was studied, the quantitative relationship between the P-wave velocity and the damage degree was obtained, and the acoustic emission (AE) count and energy evolution characteristics of specimens with different damage degrees were analyzed. The energy evolution law of damaged specimens was revealed, and with the increase in damage degree, the elastic energy stored in the specimens can be converted into crack propagation more quickly, and the dissipated energy density increases rapidly, resulting in complete rock failure. The research results can provide theoretical support for the stability analysis and control of underground engineering rock mass in the event of multiple disturbances.

Description, Microalgae-based drug delivery system exhibits efficient therapeutic effects on intestinal diseases., Design of innovative strategies for oral drug delivery is particularly promising for intestinal disease treatment. However, many obstacles such as poor therapeutic efficacy and low bioavailability and biocompatibility remain to be addressed. Here, we report a versatile formulation based on a helical-shaped cyanobacterium, Spirulina platensis (SP), loaded with curcumin (SP@Curcumin) for the treatment of colon cancer and colitis, two types of intestinal diseases. In radiotherapy for colon cancer, SP@Curcumin could mediate combined chemo- and radiotherapy to inhibit tumor progression while acting as a radioprotector to scavenge reactive oxygen species induced by the high dose of x-ray radiation in healthy tissues. SP@Curcumin could also reduce the production of proinflammatory cytokines and thereby exerted anti-inflammatory effects against colitis. The oral drug delivery system not only leveraged the biological properties of microalgal carriers to improve the bioavailability of loaded drugs but also performed excellent antitumor and anti-inflammation efficacy for intestinal disease treatment.

With rapid increase in anthropogenic CO2emissions, greenhouse effect has been more, more severe, climate change becomes a threat that influences all live beings on earth. In order to mitigate CO2emissions, Paris Agreement was signed in 2015 which requires all countries to contribute their efforts in order to limit the global average temperature increase below 2 °C by 2050.

Binary information table, multi-valued information table and set-valued information table are three kinds of information systems often encountered in information processing. For any information system, we can often induce different information granular structures, and then construct the corresponding rough set models. Generally speaking, for the same information system, three models of Pawlak rough set, covering rough set and multi-granulation rough set can be induced according to different rules. These three kinds of rough set models are effective tools for data mining and information processing. This paper studies the relationship among Pawlak rough set, covering rough set and multi granularity rough set induced in binary information table, multi-valued information table and set-valued information table, and obtains many important conclusions. The research content of this paper effectively connects the theories, methods and applications of Pawlak rough set, covering rough set and multi granularity rough set, which not only enriches the rough set theory, but also expands the application prospect of rough set.

Multifunctional antimicrobial strategies are urgently needed to treat methicillin-resistant

Particle settling in inclined channels is an important phenomenon that occurs during hydraulic fracturing of shale gas production. Generally, in order to accurately simulate the large-scale (field-scale) proppant transport process, constructing a fast and accurate sub-scale proppant settling model, or surrogate model, becomes a critical issue, since mapping between physical parameters and proppant settling velocity is complex. Previously, particle settling has usually been investigated via high-fidelity experiments and meso-scale numerical simulations, both of which are time-consuming. In this work, a new method is proposed and utilized, i.e., the multi-fidelity neural network (MFNN), to construct a settling surrogate model, which could utilize both high-fidelity and low-fidelity (thus, less expensive) data. The results demonstrate that constructing the settling surrogate with the MFNN can reduce the need for high-fidelity data and thus computational cost by 80%, while the accuracy lost is less than 5% compared to a high-fidelity surrogate. Moreover, the investigated particle settling surrogate is applied in macro-scale proppant transport simulation, which shows that the settling model is significant to proppant transport and yields accurate results. This opens novel pathways for rapidly predicting proppant settling velocity in reservoir applications., 25 pages, 15 figures

Data-driven discovery of partial differential equations (PDEs) has achieved considerable development in recent years. Several aspects of problems have been resolved by sparse regression-based and neural network-based methods. However, the performances of existing methods lack stability when dealing with complex situations, including sparse data with high noise, high-order derivatives and shock waves, which bring obstacles to calculating derivatives accurately. Therefore, a robust PDE discovery framework, called the robust deep learning-genetic algorithm (R-DLGA), that incorporates the physics-informed neural network (PINN), is proposed in this work. In the framework, a preliminary result of potential terms provided by the deep learning-genetic algorithm is added into the loss function of the PINN as physical constraints to improve the accuracy of derivative calculation. It assists to optimize the preliminary result and obtain the ultimately discovered PDE by eliminating the error compensation terms. The stability and accuracy of the proposed R-DLGA in several complex situations are examined for proof-and-concept, and the results prove that the proposed framework is able to calculate derivatives accurately with the optimization of PINN and possesses surprising robustness to complex situations, including sparse data with high noise, high-order derivatives, and shock waves., 20 pages, 8 figures

The deformation and failure of a coal–rock system in a deep environment is affected by its own mechanical properties, natural endowments, and geological structures; it is very important to study the energy evolution law of coal–rock systems. For this purpose, a Particle Flow Code in 2 Dimensions (PFC2D) simulation was conducted to assess the coal–rock structure and roadway surrounding rock. The hard roof would produce a rebound “energy supply” phenomenon when the coal was destroyed, and the influence of rock strength on the energy evolution of the coal–rock combination was analyzed. In addition, the energy evolution law of roadway surrounding rock with different roof strength is studied; the energy evolution process of roof and coal seam and deep and shallow coal mass are compared, according to the energy storage characteristics of roadway surrounding rock in different areas; the partition energy storage model of roadway surrounding rock is established preliminarily and the concepts of energy storage area and energy supply area of roadway surrounding rock are proposed; the prevention and control methods of near-field rock burst in deep roadways are discussed, and the research conclusions can provide theoretical reference for the research on the mechanism of rock burst in deep coal mines.

In obesity and type 2 diabetes, numerous genes are differentially expressed, and microRNAs are involved in transcriptional regulation of target mRNAs, but miRNAs critically involved in the appetite control are not known. Here, we identified upregulation of miR-342-3p and its host gene Evl in brain and adipose tissues in C57BL/6 mice fed with high fat-high sucrose (HFHS) chow by RNA sequencing. Mir342 (-/-) mice fed with HFHS chow were protected from obesity and diabetes. The hypothalamic arcuate nucleus neurons co-express Mir342 and EVL. The percentage of activated NPY+pSTAT3+ neurons were reduced, while POMC+pSTAT3+ neurons increased in Mir342 (-/-) mice, and they demonstrated the reduction of food intake and amelioration of metabolic phenotypes. Snap25 was identified as a major target gene of miR-342-3p and the reduced expression of Snap25 may link to functional impairment hypothalamic neurons and excess of food intake. The inhibition of miR-342-3p may be a potential candidate for miRNA-based therapy.

Protecting the whole small intestine from radiation-induced intestinal injury during the radiotherapy of abdominal or pelvic solid tumors remains an unmet clinical need. Amifostine is a promising selective radioprotector for normal tissues. However, its oral application in intestinal radioprotection remains challenging. Herein, we use microalga Spirulina platensis as a microcarrier of Amifostine to construct an oral delivery system. The system shows comprehensive drug accumulation and effective radioprotection in the whole small intestine that is significantly superior to free drug and its enteric capsule, preventing the radiation-induced intestine injury and prolonging the survival without influencing the tumor regression. It also shows benefits on the gut microbiota homeostasis and long-term safety. Based on a readily available natural microcarrier, this work presents a convenient oral delivery system to achieve effective radioprotection for the whole small intestine, providing a competitive strategy with great clinical translation potential.

The operation signal of an unmanned aerial vehicle's (UAV's) datalink will change with the flight radius and attitude, which leads to a dynamic variation of the electromagnetic susceptibility (EMS) of the datalink against the external electromagnetic interference. In order to predict the electromagnetic compatibility (EMC) for the dynamic datalink of a UAV, the operation signal of the UAV's datalink and the received interference by the UAV need to be quantified and the EMS is also necessary to be tested. In this paper, a new model of the UAV's datalink in different flight states is presented. The relationship among the airborne operation signal, the interference and the flight parameters is presented and compared in three typical flight states, i.e., the fixed angle state, dive state, and hover state. To increase the repeatability and accuracy of the test and avoid risks in a flight test, a novel EMS test method using a static test instead of a dynamic flight test is proposed. On the basis of the operation state and its corresponding EMS threshold, the EMC prediction is performed to determine the moment when the datalink will be interrupted. The new method can be used to improve the security of UAV in the severe electromagnetic environment.

With the advent of modern data collection and storage technologies, data-driven approaches have been developed for discovering the governing partial differential equations (PDE) of physical problems. However, in the extant works the model parameters in the equations are either assumed to be known or have a linear dependency. Therefore, most of the realistic physical processes cannot be identified with the current data-driven PDE discovery approaches. In this study, an innovative framework is developed that combines data-driven and data-assimilation methods for simultaneously identifying physical processes and inferring model parameters. Spatiotemporal measurement data are first divided into a training data set and a testing data set. Using the training data set, a data-driven method is developed to learn the governing equation of the considered physical problem by identifying the occurred (or dominated) processes and selecting the proper empirical model. Through introducing a prediction error of the learned governing equation for the testing data set, a data-assimilation method is devised to estimate the uncertain model parameters of the selected empirical model. For the contaminant solute transport problem investigated, the results demonstrate that the proposed method can adequately identify the considered physical processes via concurrently discovering the corresponding governing equations and inferring uncertain parameters of nonlinear models, even in the presence of measurement errors. This work helps to broaden the applicable area of the research of data driven discovery of governing equations of physical problems.

Governing equations of physical problems are traditionally derived from conservation laws or physical principles. However, some complex problems still exist for which these first-principle derivations cannot be implemented. As data acquisition and storage ability have increased, data-driven methods have attracted great attention. In recent years, several works have addressed how to learn dynamical systems and partial differential equations using data-driven methods. Along this line, in this work, we investigate how to discover subsurface flow equations from data via a machine learning technique, the least absolute shrinkage and selection operator (LASSO). The learning of single-phase groundwater flow equation and contaminant transport equation are demonstrated. Considering that the parameters of subsurface formation are usually heterogeneous, we propose a procedure for learning partial differential equations with heterogeneous model parameters for the first time. Derivative calculation from discrete data is required for implementing equation learning, and we discuss how to calculate derivatives from noisy data. For a series of cases, the proposed data-driven method demonstrates satisfactory results for learning subsurface flow equations.

Although the Brunauer-Emmett-Teller (BET) equation is a classic adsorption model for describing the adsorption of gases in adsorbents, it cannot be applied in supercritical conditions because the saturation vapor pressure (p0) in this equation is not defined when T > Tc. In this study, a modified BET equation is proposed, and can be applied to investigate supercritical methane adsorption mechanisms in shale by using density instead of pressure in this equation. The observed (excess) high-pressure methane adsorption isotherms always can be well-fitted by the modified BET model when the adsorbed-phase density (ρa) is not fixed. The fitted results show that the number of adsorption layers (n) ranges from 1.79 to 2.42, with an average value of 2.12, indicating a double-layer adsorption mechanism approximately. Moreover, we compare this novel model with the commonly used Langmuir and DR models, and find that all the three models can fit the excess adsorption isotherms equally well. However, a critical advantage of this new model is that it can calculate the number of adsorption layers (n), while other models cannot. It is this advantage that makes it possible to analyze the shale gas adsorption mechanism experimentally. Moreover, the average number of adsorption layers (θ) is much smaller than the number of adsorption layers (n), indicating that there are many empty adsorption sites in the adsorption space and the density of the second layer must be less than the first layer, which is consistent with the molecular simulation results.

In this work, the proppant transport process in large-scale propagating fractures is simulated using an Eulerian–Lagrangian method. Fracture propagation is solved using the Perkins–Kern–Nordgren (PKN) model, while the fluid-particle system is solved with the multi-phase particle-in-cell (MP-PIC) method. The fluid motion is governed by volume-averaged Navier–Stokes equations, and solved using the finite volume method, and the particle motion is solved by applying Newton’s second law in a Lagrangian manner. Based on the original MP-PIC method, an extended 2D system of governing equations for fluid-particle flow is derived to solve the moving boundary problems associated with fracture propagation. By means of this method, the fluid-particle interaction is fully coupled, and the propagating fracture is considered as a prior-known boundary for the fluid and particle phases. Several numerical experiments are performed to validate the method for simulating fluid motion and proppant settling behaviors in a fracture through comparison with results in the literature. The simulation results of the 2D framework are also compared with those of 3D framework and show a good agreement. Large-scale problems of proppant transport in propagating fractures for different proppant and fracturing fluid properties, including the leak-off effect, are then simulated using this method. The Lagrangian feature of the MP-PIC method allows for flexible design of proppant injection, such as injection of proppant with multi-densities and/or multi-sizes.

Modern geological modeling techniques represent anisotropic heterogeneous formations by high-resolution grids, which can be computationally prohibitive. This motivates the upscaling process that scales-up properties defined at a fine-scale system to equivalent properties defined at a coarse-scale system. In general, analytical methods are very efficient but limited to assumptions and approximations, whereas numerical methods are more robust albeit more time-consuming. In this work, we developed an analytical method to approximate numerical solutions in a finite difference scheme with periodic boundary conditions for two-dimensional problem. Using perturbation expansion techniques and Fourier analysis, the method generates explicit formulas of tensorial equivalent conductivity considering heterogeneity and anisotropy of two-dimensional space, as well as geometry of gridblocks. It is applicable for various cases with different covariance/variagram models and a wide range of log-conductivity variances, correlation lengths, rotation angles, anisotropy ratios of fine grid conductivity, anisotropy ratios of fine grid size, and the number of fine gridblocks in a coarse gridblock. The analytical method matched well with the numerical method for the estimation of the conductivity tensor, hydraulic head, and discharge velocity. The coefficients in the analytical method need to be computed only once for any given statistics, which makes the proposed method much more efficient than the numerical method.