Differences and identification on multi-time hydrocarbon generation of carboniferous-permian coaly source rocks in the Huanghua Depression,Bohai Bay Basin

Coal is a solid combustible mineral,and coal-bearing strata have important hydrocarbon generation potential and contribute to more than 12%of the global hydrocarbon resources.However,the deposition and hydrocarbon evolution process of ancient coal-bearing strata is characterized by multiple geological times,leading to obvious distinctions in their hydrocarbon generation potential,geological processes,and production,which affect the evaluation and exploration of hydrocarbon resources derived from coaly source rocks worldwide.This study aimed to identify the differences on oil-generated parent macerals and the production of oil generated from different coaly source rocks and through different oil generation processes.Integrating with the analysis of previous tectonic burial history and hydrocarbon generation history,high-temperature and high-pressure thermal simulation experiments,organic geochemistry,and organic petrology were performed on the Carboniferous-Permian(C-P)coaly source rocks in the Huanghua Depression,Bohai Bay Basin.The oil-generated parent macerals of coal's secondary oil generation process(SOGP)were mainly hydrogen-rich collotelinite,collodetrinite,sporinite,and cutinite,while the oil-generated parent macerals of tertiary oil generation process(TOGP)were the remaining small amount of hydrogen-rich collotelinite,sporinite,and cutinite,as well as dispersed soluble organic matter and unexhausted residual hydrocarbons.Compared with coal,the oil-generated parent macerals of coaly shale SOGP were mostly sporinite and cutinite.And part of hydrogen-poor vitrinite,lacking hydrocarbon-rich macerals,and macerals of the TOGP,in addition to some remaining cutinite and a small amount of crude oil and bitumen from SOGP contributed to the oil yield.The results indicated that the changes in oil yield had a good junction between SOGP and TOGP,both coal and coaly shale had higher SOGP aborted oil yield than TOGP starting yield,and coaly shale TOGP peak oil yield was lower than SOGP peak oil yield.There were significant differences in saturated hydrocarbon and aromatic parameters in coal and coaly shale.Coal SOGP was characterized by a lower Ts/Tm and C31-homohopane22S/(22S+22R)and a higher Pr/n C17compared to coal TOGP,while the aromatic parameter methyl dibenzothiophene ratio(MDR)exhibited coaly shale TOGP was higher than coaly shale SOGP than coaly TOGP than coaly SOGP,and coal trimethylnaphthalene ratio(TNR)was lower than coaly shale TNR.Thus,we established oil generation processes and discriminative plates.In this way,we distinguished the differences between oil generation parent maceral,oil generation time,and oil production of coaly source rocks,and therefore,we provided important support for the evaluation,prediction,and exploration of oil resources from global ancient coaly source rocks.

Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.

Multi-Granularity Neighborhood Fuzzy Rough Set Model on Two Universes

The two universes multi-granularity fuzzy rough set model is an effective tool for handling uncertainty problems between two domains with the help of binary fuzzy relations. This article applies the idea of neighborhood rough sets to two universes multi-granularity fuzzy rough sets, and discusses the two-universes multi-granularity neighborhood fuzzy rough set model. Firstly, the upper and lower approximation operators are defined in the two universes multi-granularity neighborhood fuzzy rough set model. Secondly, the properties of the upper and lower approximation operators are discussed. Finally, the properties of the two universes multi-granularity neighborhood fuzzy rough set model are verified through case studies.

Low-carbon generation expansion planning considering uncertainty of renewable energy at multi-time scales

With the development of carbon electricity,achieving a low-carbon economy has become a prevailing and inevitable trend.Improving low-carbon expansion generation planning is critical for carbon emission mitigation and a lowcarbon economy.In this paper,a two-layer low-carbon expansion generation planning approach considering the uncertainty of renewable energy at multiple time scales is proposed.First,renewable energy sequences considering the uncertainty in multiple time scales are generated based on the Copula function and the probability distribution of renewable energy.Second,a two-layer generation planning model considering carbon trading and carbon capture technology is established.Specifically,the upper layer model optimizes the investment decision considering the uncertainty at a monthly scale,and the lower layer one optimizes the scheduling considering the peak shaving at an hourly scale and the flexibility at a 15-minute scale.Finally,the results of different influence factors on low-carbon generation expansion planning are compared in a provincial power grid,which demonstrate the effectiveness of the proposed model.

Influence of Processing Parameters on Granularity Distribution of Superalloy Powders during PREP

In order to investigate the influence of processing parameters on the granularity distribution of superalloy powders during the atomization of plasma rotating electrode processing (PREP), in this paper FGH95 superalloy powders is prepared under different processing conditions by PREP and the influence of PREP processing parameters on the granularity distribution of FGH95 superalloy powders is discussed based on fractal geometry theory. The results show that with the increase of rotating velocity of the self-consuming electrode, the fractal dimension of the granularity distribution increases linearly, which results in the increase of the proportion of smaller powders. The change of interval between plasma gun and the self-consuming electrode has a little effect on the granularity distribution, also the fractal dimension of the granularity distribution changed a little correspondingly.

Bio-Inspired Optimal Dispatching of Wind Power Consumption Considering Multi-Time Scale Demand Response and High-Energy Load Participation

Bio-inspired computer modelling brings solutions fromthe living phenomena or biological systems to engineering domains.To overcome the obstruction problem of large-scale wind power consumption in Northwest China,this paper constructs a bio-inspired computer model.It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load.First,the principle of wind power obstruction with the involvement of a high-energy load is examined in this work.In this step,highenergy load model with different regulation characteristics is established.Then,considering the multi-time scale characteristics of high-energy load and other demand-side resources response speed,a multi-time scale model of coordination optimization is built.An improved bio-inspired model incorporating particle swarm optimization is applied to minimize system operation and wind curtailment costs,as well as to find the most optimal energy configurationwithin the system.Lastly,we take an example of regional power grid in Gansu Province for simulation analysis.Results demonstrate that the suggested scheduling strategy can significantly enhance the wind power consumption level and minimize the system’s operational cost.

Integrating Coarse Granularity Part-Level Features with Supervised Global-Level Features for Person Re-Identification

Person re-identification(Re-ID)has achieved great progress in recent years.However,person Re-ID methods are still suffering from body part missing and occlusion problems,which makes the learned representations less reliable.In this paper,we pro⁃pose a robust coarse granularity part-level network(CGPN)for person Re-ID,which ex⁃tracts robust regional features and integrates supervised global features for pedestrian im⁃ages.CGPN gains two-fold benefit toward higher accuracy for person Re-ID.On one hand,CGPN learns to extract effective regional features for pedestrian images.On the other hand,compared with extracting global features directly by backbone network,CGPN learns to extract more accurate global features with a supervision strategy.The single mod⁃el trained on three Re-ID datasets achieves state-of-the-art performances.Especially on CUHK03,the most challenging Re-ID dataset,we obtain a top result of Rank-1/mean av⁃erage precision(mAP)=87.1%/83.6%without re-ranking.

Multi-time scale analysis of precipitation variation in Guyuan, China:1957-2005

Morlet wavelet transformation is used in this paper to analyze the multi time scale characteristics of pre cipitation data series from 1957 to 2005 in Guyuan region.The results showed that(1) the annual precipitation evo lution process had obvious multi time scale variation characteristics of 15 25 years,7 12 years and 3 6 years,and different time scales had different oscillation energy densities;(2) the periods at smaller time scales changed more frequently,which often nested in a biggish quasi periodic oscillations,so the concrete time domain should be ana lyzed if necessary;(3) the precipitation had three main periods(22 year,9 year and 4 year) and the 22 year period was especially outstanding,and the analysis of this main period reveals that the precipitation would be in a relative high water period until about 2012.

A HYBRID GRANULARITY PARALLEL ALGORITHM FOR PRECISE INTEGRATION OF STRUCTURAL DYNAMIC RESPONSES

Precise integration methods to solve structural dynamic responses and the corresponding time integration formula are composed of two parts： the multiplication of an exponential matrix with a vector and the integration term. The second term can be solved by the series solution. Two hybrid granularity parallel algorithms are designed, that is, the exponential matrix and the first term are computed by the fine-grained parallel algorithra and the second term is computed by the coarse-grained parallel algorithm. Numerical examples show that these two hybrid granularity parallel algorithms obtain higher speedup and parallel efficiency than two existing parallel algorithms.

Fluidization and Transport of Vibrated Granular Matter:A Review of Landmark and Recent Contributions

We present a short retrospective review of the existing literature about the dynamics of(dry)granular matter under the effect of vibrations.The main objective is the development of an integrated resource where vital information about past findings and recent discoveries is provided in a single treatment.Special attention is paid to those works where successful synthetic routes to as-yet unknown phenomena were identified.Such landmark results are analyzed,while smoothly blending them with a history of the field and introducing possible categorizations of the prevalent dynamics.Although no classification is perfect,and it is hard to distillate general properties out of specific observations or realizations,two possible ways to interpret the existing results are defined according to the type of forcing or the emerging(ensuing)regime of motion.In particular,first results concerning the case where vibrations and gravity are concurrent(vertical shaking)are examined,then the companion situation with vibrations perpendicular to gravity(horizontal shaking)is described.Universality classes are introduced as follows:(1)Regimes where sand self-organizes leading to highly regular geometrical“pulsating”patterns(thin layer case);(2)Regimes where the material undergoes“fluidization”and develops an internal multicellular convective state(tick layers case);(3)Regimes where the free interface separating the sand from the overlying gas changes inclination or develops a kind a patterned configuration consisting of stable valleys and mountains or travelling waves;(4)Regimes where segregation is produced,i.e.,particles of a given size tend to be separated from the other grains(deep containers).Where possible,an analogy or parallelism is drawn with respect to the companion field of fluid-dynamics for which the assumption of“continuum”can be applied.

On Granularity in Information Systems Based on Binary Relation

In this paper, some important issues of granularity are discussed mainly in information systems (ISs) based on binary relation. Firstly, the vector representation method of knowledge granules is proposed in an infor-mation system based on binary relation to eliminate limitations of set representation method. Secondly, operators among knowledge granularity are introduced and some important properties of them are studied carefully. Thirdly, distance between two knowledge granules is established and granular space is constructed based on it. Fourthly, axiomatic definition of knowledge granularity is investigated, and one can find that some existed knowledge granularities are special cases under the definition. In addition, as an application of knowledge granular space, an example is employed to validate some results in our work.

Market-based control strategy for long-span structures considering the multi-time delay issue

To solve the different time delays that exist in the control device installed on spatial structures, in this study, discrete analysis using a 2N precise algorithm was selected to solve the multi-time-delay issue for long-span structures based on the market-based control （MBC） method. The concept of interval mixed energy was introduced from computational structural mechanics and optimal control research areas, and it translates the design of the MBC multi-time-delay controller into a solution for the segment matrix. This approach transforms the serial algorithm in time to parallel computing in space, greatly improving the solving efficiency and numerical stability. The designed controller is able to consider the issue of time delay with a linear controlling force combination and is especially effective for large time-delay conditions. A numerical example of a long-span structure was selected to demonstrate the effectiveness of the presented controller, and the time delay was found to have a significant impact on the results.

Dynamic Distribution Model with Prime Granularity for Parallel Computing

Dynamic distribution model is one of the best schemes for parallel volume rendering. How- ever, in homogeneous cluster system.since the granularity is traditionally identical, all processors communicate almost simultaneously and computation load may lose balance. Due to problems above, a dynamic distribution model with prime granularity for parallel computing is presented. Granularities of each processor are relatively prime, and related theories are introduced. A high parallel performance can be achieved by minimizing network competition and using a load balancing strategy that ensures all processors finish almost simultaneously. Based on Master-Slave-Gleaner ( MSG) scheme, the parallel Splatting Algorithm for volume rendering is used to test the model on IBM Cluster 1350 system. The experimental results show that the model can bring a considerable improvement in performance, including computation efficiency, total execution time, speed, and load balancing.

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.

Granular behaviour under bi-directional shear with constant vertical stress and constant volume

This paper aims to investigate the role of bi-directional shear in the mechanical behaviour of granular materials and macro-micro relations by conducting experiments and discrete element method(DEM)modelling.The bi-directional shear consists of a static shear consolidation and subsequent shear under constant vertical stress and constant volume conditions.A side wall node loading method is used to exert bi-directional shear of various angles.The results show that bi-directional shear can significantly influence the mechanical behaviour of granular materials.However,the relationship between bidirectional shear and mechanical responses relies on loading conditions,i.e.constant vertical stress or constant volume conditions.The stress states induced by static shear consolidation are affected by loading angles,which are enlarged by subsequent shear,consistent with the relationship between bidirectional shear and principal stresses.It provides evidence for the dissipation of stresses accompanying static liquefaction of granular materials.The presence of bi-directional principal stress rotation(PSR)is demonstrated,which evidences why the bi-directional shear of loading angles with components in two directions results in faster dissipations of stresses with static liquefaction.Contant volume shearing leads to cross-anisotropic stress and fabric at micro-contacts,but constant vertical stress shearing leads to complete anisotropic stress and fabric at micro-contacts.It explains the differentiating relationship between stress-strain responses and fabric anisotropy under these two conditions.Micromechanical signatures such as the slip state of micro-contacts and coordination number are also examined,providing further insights into understanding granular behaviour under bi-directional shear.

An inverse analysis of fluid flow through granular media using differentiable lattice Boltzmann method

This study presents a method for the inverse analysis of fluid flow problems.The focus is put on accurately determining boundary conditions and characterizing the physical properties of granular media,such as permeability,and fluid components,like viscosity.The primary aim is to deduce either constant pressure head or pressure profiles,given the known velocity field at a steady-state flow through a conduit containing obstacles,including walls,spheres,and grains.The lattice Boltzmann method(LBM)combined with automatic differentiation(AD)(AD-LBM)is employed,with the help of the GPU-capable Taichi programming language.A lightweight tape is used to generate gradients for the entire LBM simulation,enabling end-to-end backpropagation.Our AD-LBM approach accurately estimates the boundary conditions for complex flow paths in porous media,leading to observed steady-state velocity fields and deriving macro-scale permeability and fluid viscosity.The method demonstrates significant advantages in terms of prediction accuracy and computational efficiency,making it a powerful tool for solving inverse fluid flow problems in various applications.

A novel transmission energy minimization algorithm based on fine granularity scalability

This paper proposes an optimal solution to choose the number of enhancement layers in fine granularity scalability (FGS) scheme under the constraint of minimum transmission energy, in which FGS is combined with transmission energy control, so that FGS enhancement layer transmission energy is minimized while the distortion guaranteed. By changing the bit-plane level and packet loss rate, minimum transmission energy of enhancement layer is obtained, while the expected distortion is satisfied.

Granularity of Knowledge Computed by Genetic Algorithms Based on Rough Sets Theory

Rough set philosophy hinges on the granularity of data, which is used to build all its basic concepts, like approximations, dependencies, reduction etc. Genetic Algorithms provides a general frame to optimize problem solution of complex system without depending on the domain of problem.It is robust to many kinds of problems.The paper combines Genetic Algorithms and rough sets theory to compute granular of knowledge through an example of information table. The combination enable us to compute granular of knowledge effectively.It is also useful for computer auto-computing and information processing.

Research on multi-time scale doubly-fed wind turbine test system based on FPGA+CPU heterogeneous calculation

As the proportion of renewable energy increases, the interaction between renewable energy devices and the grid continues to enhance. Therefore, the renewable energy dynamic test in a power system has become more and more important. Traditional dynamic simulation systems and digital-analog hybrid simulation systems are difficult to compromise on the economy, flexibility and accuracy. A multi-time scale test system of doubly fed induction generator based on FPGA+ CPU heterogeneous calculation is proposed in this paper. The proposed test system is based on the ADPSS simulation platform. The power circuit part of the test system is setup up using the EMT(electromagnetic transient simulation) simulation, and the control part uses the actual physical devices. In order to realize the close-loop testing for the physical devices, the power circuit must be simulated in real-time. This paper proposes a multi-time scale simulation algorithm, in which the decoupling component divides the power circuit into a large time scale system and a small time scale system in order to reduce computing effort. This paper also proposes the FPGA+CPU heterogeneous computing architecture for implementing this multitime scale simulation. In FPGA, there is a complete small time-scale EMT engine, which support the flexibly circuit modeling with any topology. Finally, the test system is connected to an DFIG controller based on Labview to verify the feasibility of the test system.

Research on the Development Trend and General Applications of the Fuzzy Rough Granularity Computing Theory

In this paper, we conduct research on the development trend and general applications of the fuzzy rough granular computing theory. Granular computing is a new concept of general information processing and computing paradigm which covers all the granularity the study of the theory, methods, techniques and the tools. In many areas are the basic ideas of granular computing, such as the interval analysis, rough set theory, clustering analysis and information retrieval, machine learning, database, etc. With the theory of domain known division of target concept and rule acquisition, in knowledge discovery, data mining and the pattern recognition is widely used. Under this basis, in this paper, we propose the fuzzy rough theory based computing paradigm that gains ideal performance.