Optimization Design of the Multi-Layer Cross-Sectional Layout of An Umbilical Based on the GA-GLM

Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components directly affects manufacturing,operation and storage performances of the umbilical.For the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage requirements.Considering the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional components.Then,the GA-GLM global optimization algorithm is proposed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent method.Genetic algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout solution.The feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate solution.Finally,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is higher.Meanwhile,the multi-layering strategy is effective and feasible.The design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guidance for the umbilical industry.

Multi-layer perceptron-based data-driven multiscale modelling of granular materials with a novel Frobenius norm-based internal variable

One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.

Ambient-Condition Strategy for Production of Hollow Ga_(2)O_(3)@rGO Crystalline Nanostructures Toward Efficient Lithium Storage

Crystallineγ-Ga_(2)O_(3)@rGO core-shell nanostructures are synthesized in gram scale,which are accomplished by a facile sonochemical strategy under ambient condition.They are composed of uniformγ-Ga_(2)O_(3)nanospheres encapsulated by reduced graphene oxide(rGO)nanolayers,and their formation is mainly attributed to the existed opposite zeta potential between the Ga_(2)O_(3)and rGO.The as-constructed lithium-ion batteries(LIBs)based on as-fabricatedγ-Ga_(2)O_(3)@rGO nanostructures deliver an initial discharge capacity of 1000 mAh g^(-1)at 100 mA g^(-1)and reversible capacity of 600 mAh g^(-1)under 500 mA g^(-1)after 1000 cycles,respectively,which are remarkably higher than those of pristineγ-Ga_(2)O_(3)with a much reduced lifetime of 100 cycles and much lower capacity.Ex situ XRD and XPS analyses demonstrate that the reversible LIBs storage is dominant by a conversion reaction and alloying mechanism,where the discharged product of liquid metal Ga exhibits self-healing ability,thus preventing the destroy of electrodes.Additionally,the rGO shell could act robustly as conductive network of the electrode for significantly improved conductivity,endowing the efficient Li storage behaviors.This work might provide some insight on mass production of advanced electrode materials under mild condition for energy storage and conversion applications.

Target Controllability of Multi-Layer Networks With High-Dimensional Nodes

This paper studies the target controllability of multilayer complex networked systems,in which the nodes are highdimensional linear time invariant(LTI)dynamical systems,and the network topology is directed and weighted.The influence of inter-layer couplings on the target controllability of multi-layer networks is discussed.It is found that even if there exists a layer which is not target controllable,the entire multi-layer network can still be target controllable due to the inter-layer couplings.For the multi-layer networks with general structure,a necessary and sufficient condition for target controllability is given by establishing the relationship between uncontrollable subspace and output matrix.By the derived condition,it can be found that the system may be target controllable even if it is not state controllable.On this basis,two corollaries are derived,which clarify the relationship between target controllability,state controllability and output controllability.For the multi-layer networks where the inter-layer couplings are directed chains and directed stars,sufficient conditions for target controllability of networked systems are given,respectively.These conditions are easier to verify than the classic criterion.

Mass-Based Environmental Factor and Energy Assessment of Microwave-Assisted Synthesized Transition Metal Nanostructures

This paper describes mass-based energy phase-space projection of microwave-assisted synthesis of transition metals (zinc oxide, palladium, silver, platinum, and gold) nanostructures. The projection uses process energy budget (measured in kJ) on the horizontal axes and process density (measured in kJg−1) on the vertical axes. These two axes allow both mass usage efficiency (Environmental-Factor) and energy efficiency to be evaluated for a range of microwave applicator and metal synthesis. The metrics are allied to the: second, sixth and eleventh principle of the twelve principle of Green Chemistry. This analytical approach to microwave synthesis (widely considered as a useful Green Chemistry energy source) allows a quantified dynamic environmental quotient to be given to renewable plant-based biomass associated with the reduction of the metal precursors. Thus allowing a degree of quantification of claimed “eco-friendly” and “sustainable” synthesis with regard to waste production and energy usage.

Engineering and preliminary evaluation of multiple non-equilibrium nanostructures from a single peptide amphiphile

Compared with thermodynamically equilibrium supramolecular assemblies, non-equilibrium assemblies from the same building blocks have attracted increasing attentions because their diverse structures and dynamic natures may impart the assemblies with novel functionalities. However, facile access to non-equilibrium assemblies remains a formidable challenge. Herein, we endeavored to exploit various solvent-anti-solvent methods to achieve it using peptide amphiphile C16-VVAAEE-NH_(2) as a model. Through systematical utilization of dialysis, ultrasonic and stirring-dropping methods, as well as tuning of processing parameters, we demonstrated the successful formation of diverse non-equilibrium nanostructures with distinct morphologies and structures that significantly deviate from the thermodynamically favored twisted long ribbons. Additionally, these metastable nanostructures ultimately underwent spontaneous transformation into thermodynamically stable states. The transformation processes of three representative non-equilibrium assemblies were also demonstrated and characterized in detail using transmission electron microscopy, circular dichroism spectrum, and thioflavin T fluorescence spectrum. Furthermore, non-equilibrium assemblies exhibited various degrees of cytotoxic effects, which may stem from their spontaneous, dynamic transformation and interactions with cellular membranes. This study offers valuable approaches for direct access to diverse non-equilibrium supramolecular nanostructures from self-assembling peptide, and also has implications for the development of advanced materials with unprecedented biological functions.

A flexible ultra-broadband multi-layered absorber working at 2 GHz-40 GHz printed by resistive ink

A flexible extra broadband metamaterial absorber(MMA)stacked with five layers working at 2 GHz–40 GHz is investigated.Each layer is composed of polyvinyl chloride(PVC),polyimide(PI),and a frequency selective surface(FSS),which is printed on PI using conductive ink.To investigate this absorber,both one-dimensional analogous circuit analysis and three-dimensional full-wave simulation based on a physical model are provided.Various crucial electromagnetic properties,such as absorption,effective impedance,complex permittivity and permeability,electric current distribution and magnetic field distribution at resonant peak points,are studied in detail.Analysis shows that the working frequency of this absorber covers entire S,C,X,Ku,K and Ka bands with a minimum thickness of 0.098λ_(max)(λ_(max) is the maximum wavelength in the absorption band),and the fractional bandwidth(FBW)reaches 181.1%.Moreover,the reflection coefficient is less than-10 dB at 1.998 GHz–40.056 GHz at normal incidence,and the absorptivity of the plane wave is greater than 80%when the incident angle is smaller than 50°.Furthermore,the proposed absorber is experimentally validated,and the experimental results show good agreement with the simulation results,which demonstrates the potential applicability of this absorber at 2 GHz–40 GHz.

Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.

Multi-layer network embedding on scc-based network with motif

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new solutions.It is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based networks.In previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node classification.However,the content of semantic information is quite complex.Although graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of loss.Therefore,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology network.The Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node classification.We verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

Dynamic interwell connectivity analysis of multi-layer waterflooding reservoirs based on an improved graph neural network

The analysis of interwell connectivity plays an important role in the formulation of oilfield development plans and the description of residual oil distribution. In fact, sandstone reservoirs in China's onshore oilfields generally have the characteristics of thin and many layers, so multi-layer joint production is usually adopted. It remains a challenge to ensure the accuracy of splitting and dynamic connectivity in each layer of the injection-production wells with limited field data. The three-dimensional well pattern of multi-layer reservoir and the relationship between injection-production wells can be equivalent to a directional heterogeneous graph. In this paper, an improved graph neural network is proposed to construct an interacting process mimics the real interwell flow regularity. In detail, this method is used to split injection and production rates by combining permeability, porosity and effective thickness, and to invert the dynamic connectivity in each layer of the injection-production wells by attention mechanism.Based on the material balance and physical information, the overall connectivity from the injection wells,through the water injection layers to the production layers and the output of final production wells is established. Meanwhile, the change of well pattern caused by perforation, plugging and switching of wells at different times is achieved by updated graph structure in spatial and temporal ways. The effectiveness of the method is verified by a combination of reservoir numerical simulation examples and field example. The method corresponds to the actual situation of the reservoir, has wide adaptability and low cost, has good practical value, and provides a reference for adjusting the injection-production relationship of the reservoir and the development of the remaining oil.

Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.

Multi-layer phenomena in petawatt laser-driven acceleration of heavy ions

Laser-accelerated high-flux-intensity heavy-ion beams are important for new types of accelerators.A particle-in-cell program(Smilei) is employed to simulate the entire process of Station of Extreme Light(SEL) 100 PW laser-accelerated heavy particles using different nanoscale short targets with a thickness of 100 nm Cr, Fe, Ag, Ta, Au, Pb, Th and U, as well as 200 nm thick Al and Ca. An obvious stratification is observed in the simulation. The layering phenomenon is a hybrid acceleration mechanism reflecting target normal sheath acceleration and radiation pressure acceleration, and this phenomenon is understood from the simulated energy spectrum,ionization and spatial electric field distribution. According to the stratification, it is suggested that high-quality heavy-ion beams could be expected for fusion reactions to synthesize superheavy nuclei. Two plasma clusters in the stratification are observed simultaneously, which suggest new techniques for plasma experiments as well as thinner metal targets in the precision machining process.

A Well Productivity Model for Multi-Layered Marine and Continental Transitional Reservoirs with Complex Fracture Networks

Using the typical characteristics of multi-layered marine and continental transitional gas reservoirs as a basis,a model is developed to predict the related well production rate.This model relies on the fractal theory of tortuous capillary bundles and can take into account multiple gas flow mechanisms at the micrometer and nanometer scales,as well as the flow characteristics in different types of thin layers(tight sandstone gas,shale gas,and coalbed gas).Moreover,a source-sink function concept and a pressure drop superposition principle are utilized to introduce a coupled flow model in the reservoir.A semi-analytical solution for the production rate is obtained using a matrix iteration method.A specific well is selected for fitting dynamic production data,and the calculation results show that the tight sandstone has the highest gas production per unit thickness compared with the other types of reservoirs.Moreover,desorption and diffusion of coalbed gas and shale gas can significantly contribute to gas production,and the daily production of these two gases decreases rapidly with decreasing reservoir pressure.Interestingly,the gas production from fractures exhibits an approximately U-shaped distribution,indicating the need to optimize the spacing between clusters during hydraulic fracturing to reduce the area of overlapping fracture control.The coal matrix water saturation significantly affects the coalbed gas production,with higher water saturation leading to lower production.

Formation of Natural Melanin/TiO_(2) Nanostructure Hybrids with Enhanced Optical,Thermal and Magnetic Properties as a Soft Material

The natural Melanin/TiO_(2) was synthesized by the use of ultrasonication under UV radiation.The influence of natural melanin on the structural,optical and thermal properties of TiO_(2) nanoparticles was investigated by using Fourier transform infrared spectroscopy,thermogravimetric analysis and UV-Vis spectroscopy.It was observed that incorporating natural melanin on TiO_(2) nanoparticles(TiO_(2)-Mel)occurred at 2.01 eV with a low value of Urbach energy around 100 meV indicating improvement in the crystalline structure.Magnetic measurement at room temperature showed diamagnetic behavior.Furthermore,thermal results showed that TiO_(2)-Mel is stable even at temperatures up to 400℃.According to the results obtained by the thermal stability of melanin with titanium dioxide,it can be a good candidate in many applications such as solar cells and optoelectronics.

Multi-Layer Feature Extraction with Deformable Convolution for Fabric Defect Detection

In the textile industry,the presence of defects on the surface of fabric is an essential factor in determining fabric quality.Therefore,identifying fabric defects forms a crucial part of the fabric production process.Traditional fabric defect detection algorithms can only detect specific materials and specific fabric defect types;in addition,their detection efficiency is low,and their detection results are relatively poor.Deep learning-based methods have many advantages in the field of fabric defect detection,however,such methods are less effective in identifying multiscale fabric defects and defects with complex shapes.Therefore,we propose an effective algorithm,namely multilayer feature extraction combined with deformable convolution(MFDC),for fabric defect detection.In MFDC,multi-layer feature extraction is used to fuse the underlying location features with high-level classification features through a horizontally connected top-down architecture to improve the detection of multi-scale fabric defects.On this basis,a deformable convolution is added to solve the problem of the algorithm’s weak detection ability of irregularly shaped fabric defects.In this approach,Roi Align and Cascade-RCNN are integrated to enhance the adaptability of the algorithm in materials with complex patterned backgrounds.The experimental results show that the MFDC algorithm can achieve good detection results for both multi-scale fabric defects and defects with complex shapes,at the expense of a small increase in detection time.

Vertical 3D Nanostructures Boost Efficient Hydrogen Production Coupled with Glycerol Oxidation Under Alkaline Conditions

Hydrogen production from electrolytic water is an important sustainable technology to realize renewable energy conversion and carbon neutrality.However,it is limited by the high overpotential of oxygen evolution reaction(OER)at the anode.To reduce the operating voltage of electrolyzer,herein thermodynamically favorable glycerol oxidation reaction(GOR)is proposed to replace the OER.Moreover,vertical Ni O flakes and NiMoNH nanopillars are developed to boost the reaction kinetics of anodic GOR and cathodic hydrogen evolution,respectively.Meanwhile,excluding the explosion risk of mixed H_2/O_(2),a cheap organic membrane is used to replace the expensive anion exchange membrane in the electrolyzer.Impressively,the electrolyzer delivers a remarkable reduction of operation voltage by 280 mV,and exhibits good long-term stability.This work provides a new paradigm of hydrogen production with low cost and good feasibility.

Morphology,Nanostructure,and Oxidation Reactivity of Particulate Matter Emitted by Diesel Blending with Various Aromatics

This study aims to analyze the influence of the polycyclic aromatic hydrocarbon(PAH)content in diesel on the physical and chemical properties of diesel soot particles.Four diesel fuels with different PAH content were tested on a 11.6 L direct-injection diesel engine.The raw particulate matter(PM)before the after-treatment devices was collected using the thermophoresis sampling system and the filter sampling system.A transmission electron microscope and Raman spectrometer are used to analyze the physical properties of the soot particles,including morphology,primary particle size distribution,and graphitization degree.A Fourier transform infrared spectrometer and thermogravimetric analyzer are used to characterize the surface chemical composition and oxidation reactivity of soot particles,respectively.The results show that as the PAH content in the fuel decreases,the size of the primary soot particles decreases from 29.58 to 26.70 nm.The graphitization degree of soot particles first increases and then decreases,and the relative content of the aliphatic hydrocarbon functional groups of soot particles first decreases and then increases.The T_(10),T_(50),and T_(90) of soot from high-PAH fuel are 505.3,589.3,and 623.5℃,while those from low-PAH fuel are 480.1,557.5,and 599.2℃,respectively.This indicates that exhaust PM generated by the low-PAH fuel has poor oxidation reactivity.However,as the PAH content in fuel is further decreased,the excessively high cetane number may cause uneven mixing and incomplete combustion,leading to enhanced oxidation reactivity.

Blast wave characteristics of multi-layer composite charge:Theoretical analysis,numerical simulation,and experimental validation

This article investigates the characteristics of shock wave overpressure generated by multi-layer composite charge under different detonation modes.Combining dimensional analysis and the explosion mechanism of the charge,a peak overpressure prediction model for the composite charge under singlepoint detonation and simultaneous detonation was established.The effects of the charge structure and initiation method on the overpressure field characteristics were investigated in AUTODYN simulation.The accuracy of the prediction model and the reliability of the numerical simulation method were subsequently verified in a series of static explosion experiments.The results reveal that the mass of the inner charge was the key factor determining the peak overpressure of the composite charge under single-point detonation.The peak overpressure in the radial direction improved apparently with an increase in the aspect ratio of the charge.The overpressure curves in the axial direction exhibited a multi-peak phenomenon,and the secondary peak overpressure even exceeded the primary peak at distances of 30D and 40D(where D is the charge diameter).The difference in peak overpressure among azimuth angles of 0-90°gradually decreased with an increase in the propagation distance of the shock wave.The coupled effect of the detonation energy of the inner and outer charge under simultaneous detonation improved the overpressure in both radial and axial directions.The difference in peak overpressure obtained from model prediction and experimental measurements was less than 16.4%.

Artificial Macrophage with Hierarchical Nanostructure for Biomimetic Reconstruction of Antitumor Immunity

Artificial cells are constructed from synthetic materials to imitate the biological functions of natural cells.By virtue of nanoengineering techniques,artificial cells with designed biomimetic functions provide alternatives to natural cells,showing vast potential for biomedical applications.Especially in cancer treatment,the deficiency of immunoactive macrophages results in tumor progression and immune resistance.To overcome the limitation,a BaSO_(4)@ZIF-8/transferrin(TRF)nanomacrophage(NMΦ)is herein constructed as an alternative to immunoactive macrophages.Alike to natural immunoactive macrophages,NMΦis stably retained in tumors through the specific affinity of TRF to tumor cells.Zn^(2+)as an“artificial cytokine”is then released from the ZIF-8 layer of NMΦunder tumor microenvironment.Similar as proinflammatory cytokines,Zn^(2+)can trigger cell anoikis to expose tumor antigens,which are selectively captured by the BaSO_(4)cavities.Therefore,the hierarchical nanostructure of NMΦs allows them to mediate immunogenic death of tumor cells and subsequent antigen capture for T cell activation to fabricate long-term antitumor immunity.As a proof-of-concept,the NMΦmimics the biological functions of macrophage,including tumor residence,cytokine release,antigen capture and immune activation,which is hopeful to provide a paradigm for the design and biomedical applications of artificial cells.

Tailored BiVO_(4)/In_(2)O_(3)nanostructures with boosted charge separation ability toward unassisted water splitting

The development of new heterostructures with high photoactivity is a breakthrough for the limitation of solar-driven water splitting.Here,we first introduce indium oxide(In_(2)O_(3))nanorods(NRs)as a novel electron transport layer for bismuth vanadate(BiVO_(4))with a short charge diffusion length.In_(2)O_(3)NRs reinforce the electron transport and hole blocking of BiVO_(4),surpassing the state-of-the-art photoelectrochemical performances of BiVO_(4)-based photoanodes.Also,a tannin-nickel-iron complex(TANF)is used as an oxygen evolution catalyst to speed up the reaction kinetics.The final TANF/BiVO_(4)/In_(2)O_(3)NR photoanode generates photocurrent densities of 7.1 mAcm^(−2) in sulfite oxidation and 4.2 mA cm^(−2) in water oxidation at 1.23 V versus the reversible hydrogen electrode.Furthermore,the“artificial leaf,”which is a tandem cell with a perovskite/silicon solar cell,shows a solar-to-hydrogen conversion efficiency of 6.2%for unbiased solar water splitting.We reveal significant advances in the photoactivity of TANF/BiVO_(4)/In_(2)O_(3)NRs from the tailored nanostructure and band structure for charge dynamics.