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This paper aims to establish a mathematical model for water-flooding considering the impact of fluid–solid coupling to describe the process of development for a low-permeability reservoir. The numerical simulation method was used to analyze the process of injected water channeling into the interlayer.

Some typical cores including the sandstone and the mudstone were selected to test the permeability and the stress sensitivity, and some curves of the permeability varying with the stress for the cores were obtained to demonstrate the sensitivity of the formation. Based on the experimental results and the software Eclipse and Abaqus, the main injection parameters to reduce the amount of the injected water in flowing into the interlayer were simulated.

The results indicate that the permeability of the mudstone is more sensitive to the stress than sandstone. The injection rate can be as high as possible on the condition that no crack is activated or a new fracture is created in the development. For the B82 block of Daqing oilfield, the suggested pressure of the production pressure should be around 1–3MPa, this pressure must be gradually reached to get a higher efficiency of water injection and avoid damaging the casing.

This work is beneficial to ensure stable production and provide technical support to the production of low permeability reservoirs containing an interlayer.

From the atom economy and environmentally friendly point of views, the development of clean and green approaches using ionic liquids (ILs) as recyclable catalysts has attracted increasing attention. The purpose of this study is to investigate the effect of task-specific ILs content on the one-pot three-component Biginelli reaction.

A series of halogen-free quaternary ammonium ILs functionalized with –SO₃H group were prepared and characterized by ¹H nuclear magnetic resonance (NMR), ¹³C NMR and electrospray ionization mass spectrometry. The ILs were used as catalysts for Biginelli reaction among aromatic aldehydes, urea or thiourea and β-dicarbonyl compounds. Anions and cations of ILs were varied to observe their effects on and contributions to the catalysts. The influencing factors, such as the amount of catalyst, solvent, reaction time and reaction temperature, were investigated.

The effect and contribution of cations of ILs were observed. Results showed that 3-(N, N-dimethylhexadecylammonium) propanesulfonic acid toluene sulfate ([DHPA][Tos]) showed comparable catalytic activity. Good adaptability to the reaction substrate and maximum product yield was observed when [DHPA][Tos] was used as catalyst. It was found that Biginelli reaction catalyzed by 10 mol% [DHPA][Tos] for 3 h under solvent-free conditions at 80 °C gave the best yield of 94%. Post-processing steps were simple, and the catalyst could be reused easily.

This paper demonstrates that ILs containing a long carbon chain and a bulky Tos anion efficiently promoted the reaction, in which the long carbon chains facilitate mass transfer in the reaction system.

The three-axis magnetic sensors are mostly calibrated by scalar method such as ellipsoid fitting and so on, but these methods cannot completely determine the 12 parameters of the error model. A two-stage calibration method based on particle swarm optimization (TSC-PSO) is proposed, which makes full use of the amplitude invariance and direction invariance of Earth’s magnetic field vector.

The TSC-PSO designs two-stage fitness function. Stage 1: design a fitness function of the particle swarm by the amplitude invariance of the Earth’s magnetic field to obtain a preliminary error matrix G and the bias error B. Stage 2: further design the fitness function of the particle swarm by the invariance of the Earth’s magnetic field to obtain a rotation matrix R, thereby determining the error matrix uniquely.

The proposed TSC-PSO can completely determine 12 unknown parameters in error model and further decrease the maximum fluctuation error of the Earth’s magnetic field amplitude and the absolute error of heading.

The proposed TSC-PSO provides an effective solution for three-axis magnetic sensor error compensation, which can greatly reduce the price of magnetic sensors and be used in the fields of Earth’s magnetic survey, drilling and Earth’s magnetic integrated navigation.

The proposed TSC-PSO has significantly improved the magnetic field amplitude and heading accuracy and does not require additional heading reference. In addition, the method is insensitive to noise and initialization conditions, has good robustness and can converge to a global optimum.

The sources of magnetic sensors errors are numerous, such as currents around, soft magnetic and hard magnetic materials and so on. The traditional methods mainly use explicit error models, and it is difficult to include all interference factors. This paper aims to present an implicit error model and studies its high-precision training method.

A multi-level extreme learning machine based on reverse tuning (MR-ELM) is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. To ensure the real-time performance of the algorithm, the network structure is fixed to two ELM levels, and the maximum number of levels and neurons will not be continuously increased. The parameters of MR-ELM are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time can still be guaranteed.

The results show that the training time of the MR-ELM is 19.65 s, which is about four times that of the fixed extreme learning algorithm, but training accuracy and generalization performance of the error model are better. The heading error is reduced from the pre-compensation ±2.5° to ±0.125°, and the root mean square error is 0.055°, which is about 0.46 times that of the fixed extreme learning algorithm.

MR-ELM is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. In this case, the multi-level ELM network parameters are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time training can still be guaranteed. The revised manuscript improved the ELM algorithm itself (referred to as MR-ELM) and bring new ideas to the peers in the magnetic compass error compensation field.

Prior studies have demonstrated the important role of coopetition in firms’ innovation. Based on the paradox perspective, this study aims to focus on technology transfer, the pre-innovation stage, to provide a supplementary understanding of the complementarity and contradictoriness of paradoxical coopetition, with the formal and informal governance mechanisms which are suitable with this understanding in coopetition.

This study conducted an original, multisource survey of 280 Chinese manufacturing firms. Hypotheses were tested through multiple regressions.

Coopetition has a positive impact on technology transfer between firms. Along with the increasing specificity of assets invested ex ante as a kind of formal governance mechanism, the relationship between coopetition and technology transfer becomes stronger. Meanwhile, inter-firm justice as an informal governance mechanism in the technology transfer process can be positively affected by coopetition between partners.

The study adds to the business-to-business coopetition literature on how to properly treat and use coopetition in technology transfer. Using the paradox perspective in the Chinese context, the findings emphasize the positive role of coopetition in the inter-firm technological exchange process, enriching the understanding of the complementary and contradictory features of paradoxical coopetition. To govern coopetitive relationships, the firms should also implement two fundamental governance mechanisms, that is, specialty asset and inter-firm justice.

To address the lack of data in this field and determine the relationship between the coefficient of friction and the interference between locomotive wheels and axles, this study evaluates the theoretical relationship between the coefficient of friction and the interference under elastic deformation.

When using numerical analyses to study the mechanical state of the contacting components of the wheels and axle, the interference between the axle parts and the coefficient of friction between the axle parts are two important influencing factors. Currently, as the range of the coefficient of friction between the wheel and axle in interference remains unknown, it is generally considered that the coefficient of friction is only related to the materials of the friction pair; the relationship between the interference and the coefficient of friction is often neglected.

A total of 520 press-fitting experiments were conducted for 130 sets of wheels and axles of the HXD2 locomotive with 4 types of interferences, in order to obtain the relationship between the coefficient of friction between the locomotive wheel and axle and the amount of interference. These results are expected to serve as a reference for selecting the coefficient of friction when designing axle structures with the rolling stock, research on the press-fitting process and evaluations of the fatigue life.

The study provides a basis for the selection of friction coefficient and interference amount in the design of locomotive wheels and axles.

The purpose of this paper is to study the influence of surface precision on the lubrication state of the roller chain under adequate and rare oil supply conditions, respectively.

The straightness error and roughness error of the pin generatrices were measured and the influence of surface precision on the lubrication behavior under steady state and reciprocating motion was studied through optical interference experiments.

The lubrication state is strongly influenced by the surface precision of the roller surface both under adequate oil supply and rare oil supply conditions.

In industrial applications, the machining errors of parts cannot be completely eliminated. Studying the influence of the surface precision on the lubrication behavior of pin–bush pairs can provide the experimental basis for the optimal design of the bush roller chains.

Sizing functions are crucial inputs for unstructured mesh generation since they determine the element distributions of resulting meshes to a large extent. Meanwhile, automating the procedure of creating a sizing function is a prerequisite to set up a fully automatic mesh generation pipeline. In this paper, an automatic algorithm is proposed to create a high-quality sizing function for an unstructured surface and volume mesh generation by using a triangular mesh as the background mesh.

A practically efficient and effective solution is developed by using local operators carefully to re-mesh the tessellation of the input Computer Aided Design (CAD) models. A nonlinear programming (NLP) problem has been formulated to limit the gradient of the sizing function, while in this study, the object function of this NLP is replaced by an analytical equation that predicts the number of elements. For the query of the sizing value, an improved algorithm is developed by using the axis-aligned bounding box (AABB) tree structure.

The local operations of re-meshing could effectively and efficiently resolve the banding issue caused by using the default tessellation of the model to define a sizing function. Experiments show that the solution of the revised NLP, in most cases, could provide a better solution at the lower cost of computational time. With the help of the AABB tree, the sizing function defined at a surface background mesh can be also used as the input of volume mesh generation.

Theoretical analysis reveals that the construction of the initial sizing function could be reduced to the solution of an optimization problem. The definitions of the banding elements and surface proximity are also given. Under the guidance of this theoretical analysis, re-meshing and ray-casting technologies are well-designed to initial the sizing function. Smoothing with the revised NLP and querying by the AABB tree, the paper provides an automatic method to get a high-quality sizing function for both surface and volume mesh generation.

The titania (titanium dioxide) is one of the important functional additives in the photosensitive resin and encounters the problem of stabilization in the photosensitive resin for 3D printing. This study aims to achieve enhancement in stabilization by preparation of the polymerizable titania and in situ laser-induced crystallization during 3D printing.

A type of polymerizable titania (AAEM@TiO₂) was designed and prepared from tetrabutyl titanate (TBT) and 2-(acetoacetoxy)ethyl methacrylate (AAEM) via the sol–gel process, which was characterized by Fourier-transform infrared (FTIR) spectra, ultraviolet–visible (UV-Vis) spectra, surface bonding efficiency (SBE) and settling height (H). AAEM acted on both bonding to the titania and polymerization with the monomer in resin for stabilization. The polymerizable titania could be converted to the pigmented titania by means of laser-induced crystallization. The photosensitive resin was then formulated on the basis of optimization and used in a stereolithography apparatus (SLA) for 3D printing.

The stabilization effect of AAEM on TiO₂ was achieved and the mechanism of competition in the light-consuming reactions during photocuring was proposed. The ratio of n_AAEM/n_TBT in AAEM@TiO₂, the concentration of AAEM@TiO₂ and photoinitiator (PI) used in the photosensitive resin were optimized. The anatase crystal form was indicated by X-ray diffraction (XRD) and clustering of nanocrystals was revealed by scanning electron microscopy (SEM) after SLA 3D printing.

This investigation provides a novel method of pigmentation by preparation of the polymerizable titania and in situ laser-induced crystallization for SLA 3D printing.