A hybrid physics-informed data-driven neural network for CO_(2) storage in depleted shale reservoirs

To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) sequestration models do not adequately consider multiple transport mechanisms.Moreover,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and decision-making.In this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training database.On this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and inversion.By incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion processes.Furthermore,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated scenarios.The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources.This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.

Research on Scheduling Strategy of Flexible Interconnection Distribution Network Considering Distributed Photovoltaic and Hydrogen Energy Storage

Distributed photovoltaic(PV)is one of the important power sources for building a new power system with new energy as the main body.The rapid development of distributed PV has brought new challenges to the operation of distribution networks.In order to improve the absorption ability of large-scale distributed PV access to the distribution network,the AC/DC hybrid distribution network is constructed based on flexible interconnection technology,and a coordinated scheduling strategy model of hydrogen energy storage(HS)and distributed PV is established.Firstly,the mathematical model of distributed PV and HS system is established,and a comprehensive energy storage system combining seasonal hydrogen energy storage(SHS)and battery(BT)is proposed.Then,a flexible interconnected distribution network scheduling optimization model is established to minimize the total active power loss,voltage deviation and system operating cost.Finally,simulation analysis is carried out on the improved IEEE33 node,the NSGA-II algorithm is used to solve specific examples,and the optimal scheduling results of the comprehensive economy and power quality of the distribution network are obtained.Compared with the method that does not consider HS and flexible interconnection technology,the network loss and voltage deviation of this method are lower,and the total system cost can be reduced by 3.55%,which verifies the effectiveness of the proposed method.

Coordinated planning for flexible interconnection and energy storage system in low-voltage distribution networks to improve the accommodation capacity of photovoltaic

The increasing proportion of distributed photovoltaics(DPVs)and electric vehicle charging stations in low-voltage distribution networks(LVDNs)has resulted in challenges such as distribution transformer overloads and voltage violations.To address these problems,we propose a coordinated planning method for flexible interconnections and energy storage systems(ESSs)to improve the accommodation capacity of DPVs.First,the power-transfer characteristics of flexible interconnection and ESSs are analyzed.The equipment costs of the voltage source converters(VSCs)and ESSs are also analyzed comprehensively,considering the differences in installation and maintenance costs for different installation locations.Second,a bilevel programming model is established to minimize the annual comprehensive cost and yearly total PV curtailment capacity.Within this framework,the upper-level model optimizes the installation locations and capacities of the VSCs and ESSs,whereas the lower-level model optimizes the operating power of the VSCs and ESSs.The proposed model is solved using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-II).The effectiveness of the proposed planning method is validated through an actual LVDN scenario,which demonstrates its advantages in enhancing PV accommodation capacity.In addition,the economic benefits of various planning schemes with different flexible interconnection topologies and different PV grid-connected forms are quantitatively analyzed,demonstrating the adaptability of the proposed coordinated planning method.

Shale oil development techniques and application based on ternary-element storage and flow concept in Jiyang Depression,Bohai Bay Basin,East China

The ternary-element storage and flow concept for shale oil reservoirs in Jiyang Depression of Bohai Bay Basin,East China,was proposed based on the data of more than 10000 m cores and the production of more than 60 horizontal wells.The synergy of three elements(storage,fracture and pressure)contributes to the enrichment and high production of shale oil in Jiyang Depression.The storage element controls the enrichment of shale oil;specifically,the presence of inorganic pores and fractures,as well as laminae of lime-mud rocks,in the saline lake basin,is conducive to the storage of shale oil,and the high hydrocarbon generating capacity and free hydrocarbon content are the material basis for high production.The fracture element controls the shale oil flow;specifically,natural fractures act as flow channels for shale oil to migrate and accumulate,and induced fractures communicate natural fractures to form complex fracture network,which is fundamental to high production.The pressure element controls the high and stable production of shale oil;specifically,the high formation pressure provides the drive force for the migration and accumulation of hydrocarbons,and fracturing stimulation significantly increases the elastic energy of rock and fluid,improves the imbibition replacement of oil in the pores/fractures,and reduces the stress sensitivity,guaranteeing the stable production of shale oil for a long time.Based on the ternary-element storage and flow concept,a 3D development technology was formed,with the core techniques of 3D well pattern optimization,3D balanced fracturing,and full-cycle optimization of adjustment and control.This technology effectively guides the production and provides a support to the large-scale beneficial development of shale oil in Jiyang Depression.

HUSTserver:Implementation for Reliable and High-Performance Network Attached Storage System

Network attached storage (NAS) with the properties of improved scalability, simplified management, low cost and balanced price performance, is desirable for high performance storage systems applied to extensive areas. Unfortunately, it also has some disadvantages such as increased network workload, and inconvenience in disaster recovery. To overcome these disadvantages, we propose a channel bonding technique and provide hot backup functions in the designed NAS system, named HUSTserver. Channel bonding means merging multiple Ethernet channels into integrated one, and that the data packets can be transferred through any available network channels in a parallel mode. The hot backup function provides automatic data mirroring among servers. In this paper, we first describe the whole system prototype from a software and hardware architecture view. Then, multiple Ethernet and hot backup technologies that distinguish HUSTserver from others are discussed in detail. The findings presented demonstrate that network bandwidth can be scaled by the use of multiple commodity networks. Dual parallel channels of commodity 100 Mbps Ethernet are both necessary and sufficient to support the data rates of multiple concurrent file transfers. And the hot backup function introduced in our system provides high data accessibility.

A Network-Attached Storage System Supporting Guaranteed QoS

We propose a network-attached storage system that can support guaranteed Quality of Service （QoS）, called POPNet Storage. The special policy of date access and disk scheduling is enable users to access files quickly and directly with guaranteed QoS in the POPNet Storage. The POPNet Storage implements a measurement-based admission control algorithm （PSMBAC） to determine whether to admit a new data access request stream and admit as many requests as possible while meeting the QoS guarantees to its clients. The data reconstruction algorithms in the POPNet Storage also put more emphasis on data availability and guaranteed QoS, thus it is designed to complete the data recovery as soon as possible and at the same time provide the guaranteed QoS for high-priority data access. The experiment results show that the POPNet Storage can provide more significant performance, reliability, and guaranteed QoS than conventional storage systems.

Grid Side Distributed Energy Storage Cloud Group End Region Hierarchical Time-Sharing Configuration Algorithm Based onMulti-Scale and Multi Feature Convolution Neural Network

There is instability in the distributed energy storage cloud group end region on the power grid side.In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capacitor components showa continuous and stable charging and discharging state,a hierarchical time-sharing configuration algorithm of distributed energy storage cloud group end region on the power grid side based on multi-scale and multi feature convolution neural network is proposed.Firstly,a voltage stability analysis model based onmulti-scale and multi feature convolution neural network is constructed,and the multi-scale and multi feature convolution neural network is optimized based on Self-OrganizingMaps(SOM)algorithm to analyze the voltage stability of the cloud group end region of distributed energy storage on the grid side under the framework of credibility.According to the optimal scheduling objectives and network size,the distributed robust optimal configuration control model is solved under the framework of coordinated optimal scheduling at multiple time scales;Finally,the time series characteristics of regional power grid load and distributed generation are analyzed.According to the regional hierarchical time-sharing configuration model of“cloud”,“group”and“end”layer,the grid side distributed energy storage cloud group end regional hierarchical time-sharing configuration algorithm is realized.The experimental results show that after applying this algorithm,the best grid side distributed energy storage configuration scheme can be determined,and the stability of grid side distributed energy storage cloud group end region layered timesharing configuration can be improved.

Security System in United Storage Network and Its Implementation

With development of networked storage and its applications, united storage network (USN) combined with network attached storage (NAS) and storage area network (SAN) has emerged. It has such advantages as high performance, low cost, good connectivity, etc. However the security issue has been complicated because USN responds to block I/O and file I/O requests simultaneously. In this paper, a security system module is developed to prevent many types of attacks against USN based on NAS head. The module not only uses effective authentication to prevent unauthorized access to the system data, but also checks the data integrity. Experimental results show that the security module can not only resist remote attacks and attacks from those who has physical access to the USN, but can also be seamlessly integrated into underlying file systems, with little influence on their performance.

File Assignment Policy in Network Storage System

Network storage increase capacity and scalability of storage system, data availability and enables the sharing of data among clients. When the developing network technology reduce performance gap between disk and network, however, mismatched policies and access pattern can significantly reduce network storage performance. So the strategy of data placement in system is an important factor that impacts the performance of overall system. In this paper, the two algorithms of file assignment are presented. One is Greed partition that aims at the load balance across all NADs (Network Attached Disk). The other is Sort partition that tries to minimize variance of service time in each NAD. Moreover, we also compare the performance of our two algorithms in practical environment. Our experimental results show that when the size distribution (load characters) of all assigning files is closer and larger, Sort partition provides consistently better response times than Greedy algorithm. However, when the range of all assigning files is wider, there are more small files and access rate is higher, the Greedy algorithm has superior performance in compared with the Sort partition in off-line.

Analysis models and methods on the network storage system I/O performance

Network storage provides high scalability, availability and flexibility for storage systems, and is widely applied to many fields. Particularly, I/O performance is of great significance. Its application is wide and expanding rapidly. I/O performance has already become the bottleneck of the whole performance of computer systems for a long time, and under the condition of the present computer technology, I/O performance optimization method looks especially important. In the paper, I/O performance model was analyzed based on the combination of quasi birth, death process and queuing model, and then solved the model. A number of important related performance indicators and the relationship between them were given. By the way of example, this method can show the I/O performance more accurately. Finally, we got some useful conclusions, which may be used to evaluate network storage performance, and are the basis of confirming I/O scheduling strategy.

Beam based alignment using a neural network

Beams typically do not travel through the magnet centers because of errors in storage rings.The beam deviating from the quadrupole centers is affected by additional dipole fields due to magnetic field feed-down.Beam-based alignment(BBA)is often performed to determine a golden orbit where the beam circulates around the quadrupole center axes.For storage rings with many quadrupoles,the conventional BBA procedure is time-consuming,particularly in the commissioning phase,because of the necessary iterative process.In addition,the conventional BBA method can be affected by strong coupling and the nonlinearity of the storage ring optics.In this study,a novel method based on a neural network was proposed to determine the golden orbit in a much shorter time with reasonable accuracy.This golden orbit can be used directly for operation or adopted as a starting point for conventional BBA.The method was demonstrated in the HLS-II storage ring for the first time through simulations and online experiments.The results of the experiments showed that the golden orbit obtained using this new method was consistent with that obtained using the conventional BBA.The development of this new method and the corresponding experiments are reported in this paper.

Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors,site quality,and aridity index

The diameter distribution function(DDF)is a crucial tool for accurately predicting stand carbon storage(CS).The current key issue,however,is how to construct a high-precision DDF based on stand factors,site quality,and aridity index to predict stand CS in multi-species mixed forests with complex structures.This study used data from70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest,Hebei Province,China,to construct the DDF based on maximum likelihood estimation and finite mixture model(FMM).Ordinary least squares(OLS),linear seemingly unrelated regression(LSUR),and back propagation neural network(BPNN)were used to investigate the influences of stand factors,site quality,and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests.The results showed that FMM accurately described the stand-level diameter distribution of the mixed P.davidiana and B.platyphylla forests;whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution.The combined variable of quadratic mean diameter(Dq),stand basal area(BA),and site quality improved the accuracy of the shape parameter models of FMM;the combined variable of Dq,BA,and De Martonne aridity index improved the accuracy of the scale parameter models.Compared to OLS and LSUR,the BPNN had higher accuracy in the re-parameterization process of FMM.OLS,LSUR,and BPNN overestimated the CS of P.davidiana but underestimated the CS of B.platyphylla in the large diameter classes(DBH≥18 cm).BPNN accurately estimated stand-and species-level CS,but it was more suitable for estimating stand-level CS compared to species-level CS,thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests.

Data Secure Storage Mechanism of Sensor Networks Based on Blockchain

As the number of sensor network application scenarios continues to grow,the security problems inherent in this approach have become obstacles that hinder its wide application.However,it has attracted increasing attention from industry and academia.The blockchain is based on a distributed network and has the characteristics of non-tampering and traceability of block data.It is thus naturally able to solve the security problems of the sensor networks.Accordingly,this paper first analyzes the security risks associated with data storage in the sensor networks,then proposes using blockchain technology to ensure that data storage in the sensor networks is secure.In the traditional blockchain,the data layer uses a Merkle hash tree to store data;however,the Merkle hash tree cannot provide non-member proof,which makes it unable to resist the attacks of malicious nodes in networks.To solve this problem,this paper utilizes a cryptographic accumulator rather than a Merkle hash tree to provide both member proof and non-member proof.Moreover,the number of elements in the existing accumulator is limited and unable to meet the blockchain’s expansion requirements.This paper therefore proposes a new type of unbounded accumulator and provides its definition and security model.Finally,this paper constructs an unbounded accumulator scheme using bilinear pairs and analyzes its performance.

Performance analysis of an iSCSI-based unified storage network

In this paper, we introduced a novel storage architecture 'Unified Storage Network', which merges NAC( Network Attached Channel) and SAN( Storage Area Network) , and provides the file I/O services as NAS devices and provides the block I/O services as SAN. To overcome the drawbacks from FC, we employ iSCSI to implement the USN( Unified Storage Network) . To evaluate whether iSCSI is more suitable for implementing the USN, we analyze iSCSI protocol and compare it with FC protocol from several components of a network protocol which impact the performance of the network. From the analysis and comparison, we can conclude that the iSCSI is more suitable for implementing the storage network than the FC under condition of the wide-area network. At last, we designed two groups of experiments carefully.

GRSA: Service-Aware Flow Scheduling for Cloud Storage Datacenter Networks

The proliferation of the global datasphere has forced cloud storage systems to evolve more complex architectures for different applications.The emergence of these application session requests and system daemon services has created large persistent flows with diverse performance requirements that need to coexist with other types of traffic.Current routing methods such as equal-cost multipath(ECMP)and Hedera do not take into consideration specific traffic characteristics nor performance requirements,which make these methods difficult to meet the quality of service(QoS)for high-priority flows.In this paper,we tailored the best routing for different kinds of cloud storage flows as an integer programming problem and utilized grey relational analysis(GRA)to solve this optimization problem.The resulting method is a GRAbased service-aware flow scheduling(GRSA)framework that considers requested flow types and network status to select appropriate routing paths for flows in cloud storage datacenter networks.The results from experiments carried out on a real traffic trace show that the proposed GRSA method can better balance traffic loads,conserve table space and reduce the average transmission delay for high-priority flows compared to ECMP and Hedera.

Using RBF Neural Network for OptimumControl of a Cold Storage

In recent years, advanced control technologies have been used for the optimum control of a cold storage. But there are still a lot of shortcomings. One of the main problems is that the traditional methods can't realize the on-line predictive optimum control of a refrigerating system with simple and valid algorithms. An RBF neural network has a strong ability in nonlinear mapping, a good interpolating value performance, and a higher training speed. Thus a two-stage RBF neural network is proposed in this paper. Combining the measured values with the predicted values, the two-stage RBF neural network is used for the on-line predictive optimum control of the cold storage temperature. The application results of the new methods show a great success.

Research on the Security of the United Storage Network Based on NAS

A multi-user view file system (MUVFS) and a security scheme are developed to improve the security of the united storage network (USN) that integrates a network attached storage (NAS) and a storage area network (SAN). The MUVFS offers a storage volume view for each authorized user who can access only the data in his own storage volume, the security scheme enables all users to encrypt and decrypt the data of their own storage view at client-side, and the USN server needs only to check the users’ identities and the data’s integrity. Experiments were performed to compare the sequential read, write and read/write rates of NFS+MUVFS+secure_module with those of NFS. The results indicate that the security of the USN is improved greatly with little influence on the system performance when the MUVFS and the security scheme are integrated into it.

NASSN:a NAS-based storage network

With the digital information and application requirement on the Internet increasing fleetly nowadays,it is urgent to work out a network storage system with a large capacity,a high availability and scalability.To solve the above-mentioned issues,a NAS-based storage network(for short NASSN)has been designed.Firstly,the NASSN integrates multi-NAS,iNAS(an iSCSI-based NAS)and enterprise SAN with the help of storage virtualization,which can provide a greater capacity and better scalability.Secondly,the NASSN can provide high availability with the help of server and storage subsystem redundancy technologies.Thirdly,the NASSN simultaneously serves for both the file I/O and the block I/O with the help of an iSCSI module,which has the advantages of NAS and SAN.Finally,the NASSN can provide higher I/O speed by a high network-attached channel which implements the direct data transfer between the storage device and client.In the experiments,the NASSN has ultra-high-throughput for both of the file I/O requests and the block I/O requests.

A Network Coding Based Cloud Storage Scheme

With this paper, we propose a network coding based cloud storage scheme. The storage system is in the form of an m * n data array. The n columns stand for n storage nodes, which are comprised of a part of systematic nodes storing source symbols and a part of nonsystematic nodes storing parity symbols. Every row of the data array is a (n, k) systematic Maximum Distance Separable (MDS) code. A source symbol is only involved in the encoding with the unique row;it locates at and is not used by other rows. Such a design significantly decreases the complexity of encoding and decoding. Moreover, in case of single node failures, we use interference alignment to further reduce repair bandwidth. Compared to some existing cloud storage schemes, our scheme significantly reduces resource consumption on storage, update bandwidth and repair bandwidth.

Deep Learning Network for Energy Storage Scheduling in Power Market Environment Short-Term Load Forecasting Model

In the electricity market,fluctuations in real-time prices are unstable,and changes in short-term load are determined by many factors.By studying the timing of charging and discharging,as well as the economic benefits of energy storage in the process of participating in the power market,this paper takes energy storage scheduling as merely one factor affecting short-term power load,which affects short-term load time series along with time-of-use price,holidays,and temperature.A deep learning network is used to predict the short-term load,a convolutional neural network(CNN)is used to extract the features,and a long short-term memory(LSTM)network is used to learn the temporal characteristics of the load value,which can effectively improve prediction accuracy.Taking the load data of a certain region as an example,the CNN-LSTM prediction model is compared with the single LSTM prediction model.The experimental results show that the CNN-LSTM deep learning network with the participation of energy storage in dispatching can have high prediction accuracy for short-term power load forecasting.