User Instructions for the Dynamic Database of Solid-State Electrolyte 2.0(DDSE 2.0)

The Dynamic Database of Solid-State Electrolyte(DDSE)is an advanced online platform offering a comprehensive suite of tools for solid-state battery research and development.Its key features include statistical analysis of both experimental and computational solid-state electrolyte(SSE)data,interactive visualization through dynamic charts,user data assessment,and literature analysis powered by a large language model.By facilitating the design and optimization of novel SSEs,DDSE serves as a critical resource for advancing solid-state battery technology.This Technical Report provides detailed tutorials and practical examples to guide users in effectively utilizing the platform.

ARCHITECTURE OF DYNAMIC DATA DRIVEN SIMULATION FOR WILDFIRE AND ITS REALIZATION

Dynamic data driven simulation （DDDS） is proposed to improve the model by incorporaing real data from the practical systems into the model. Instead of giving a static input, multiple possible sets of inputs are fed into the model. And the computational errors are corrected using statistical approaches. It involves a variety of aspects, including the uncertainty modeling, the measurement evaluation, the system model and the measurement model coupling ,the computation complexity, and the performance issue. Authors intend to set up the architecture of DDDS for wildfire spread model, DEVS-FIRE, based on the discrete event speeification （DEVS） formalism. The experimental results show that the framework can track the dynamically changing fire front based on fire sen- sor data, thus, it provides more aecurate predictions.

A Novel Robust Nonlinear Dynamic Data Reconciliation

Outlier in one variable will smear the estimation of other measurements in data reconciliation （DR）. In this article, a novel robust method is proposed for nonlinear dynamic data reconciliation, to reduce the influence of outliers on the result of DR. This method introduces a penalty function matrix in a conventional least-square objective function, to assign small weights for outliers and large weights for normal measurements. To avoid the loss of data information, element-wise Mahalanobis distance is proposed, as an improvement on vector-wise distance, to construct a penalty function matrix. The correlation of measurement error is also considered in this article. The method introduces the robust statistical theory into conventional least square estimator by constructing the penalty weight matrix and gets not only good robustness but also simple calculation. Simulation of a continuous stirred tank reactor, verifies the effectiveness of the proposed algorithm.

Differential Privacy Preserving Dynamic Data Release Scheme Based on Jensen-Shannon Divergence

Health monitoring data or the data about infectious diseases such as COVID-19 may need to be constantly updated and dynamically released,but they may contain user's sensitive information.Thus,how to preserve the user's privacy before their release is critically important yet challenging.Differential Privacy(DP)is well-known to provide effective privacy protection,and thus the dynamic DP preserving data release was designed to publish a histogram to meet DP guarantee.Unfortunately,this scheme may result in high cumulative errors and lower the data availability.To address this problem,in this paper,we apply Jensen-Shannon(JS)divergence to design the OPTICS(Ordering Points To Identify The Clustering Structure)scheme.It uses JS divergence to measure the difference between the updated data set at the current release time and private data set at the previous release time.By comparing the difference with a threshold,only when the difference is greater than the threshold,can we apply OPTICS to publish DP protected data sets.Our experimental results show that the absolute errors and average relative errors are significantly lower than those existing works.

Dynamic data-sharing based user recruitment in mobile crowdsensing

Mobile crowdsensing(MCS) has become an emerging paradigm to solve urban sensing problems by leveraging the ubiquitous sensing capabilities of the crowd. One critical issue in MCS is how to recruit users to fulfill more sensing tasks with budget restriction, while sharing data among tasks can be a credible way to improve the efficiency. The data-sharing based user recruitment problem under budget constraint in a realistic scenario is studied, where multiple tasks require homogeneous data but have various spatio-temporal execution ranges, meanwhile users suffer from uncertain future positions. The problem is formulated in a manner of probability by predicting user mobility, then a dynamic user recruitment algorithm is proposed to solve it. In the algorithm a greedy-adding-and-substitution(GAS) heuristic is repeatedly implemented by updating user mobility prediction in each time slot to gradually achieve the final solution. Extensive simulations are conducted using a real-world taxi trace dataset, and the results demonstrate that the approach can fulfill more tasks than existing methods.

Study of inter-well interference in shale gas reservoirs by a robust production data analysis method based on deconvolution

In order to overcome the defects that the analysis of multi-well typical curves of shale gas reservoirs is rarely applied to engineering,this study proposes a robust production data analysis method based on deconvolution,which is used for multi-well inter-well interference research.In this study,a multi-well conceptual trilinear seepage model for multi-stage fractured horizontal wells was established,and its Laplace solutions under two different outer boundary conditions were obtained.Then,an improved pressure deconvolution algorithm was used to normalize the scattered production data.Furthermore,the typical curve fitting was carried out using the production data and the seepage model solution.Finally,some reservoir parameters and fracturing parameters were interpreted,and the intensity of inter-well interference was compared.The effectiveness of the method was verified by analyzing the production dynamic data of six shale gas wells in Duvernay area.The results showed that the fitting effect of typical curves was greatly improved due to the mutual restriction between deconvolution calculation parameter debugging and seepage model parameter debugging.Besides,by using the morphological characteristics of the log-log typical curves and the time corresponding to the intersection point of the log-log typical curves of two models under different outer boundary conditions,the strength of the interference between wells on the same well platform was well judged.This work can provide a reference for the optimization of well spacing and hydraulic fracturing measures for shale gas wells.

EVOLUTION AND INTERACTION OF δ-WAVES IN THE ZERO-PRESSURE GAS DYNAMICS SYSTEM

Evolution and interaction of plane waves of the multidimensional zero-pressure gas dynamics system leads to the study of the corresponding one dimensional system.In this paper,we study the initial value problem for one dimensional zero-pressure gas dynamics system.Here the first equation is the Burgers equation and the second one is the continuity equation.We consider the solution with initial data in the space of bounded Borel measures.First we prove a general existence result in the algebra of generalized functions of Colombeau.Then we study in detail special solutions withδ-measures as initial data.We study interaction of waves originating from initial data concentrated on two point sources and interaction with classical shock/rarefaction waves.This gives an understanding of plane-wave interactions in the multidimensional case.We use the vanishing viscosity method in our analysis as this gives the physical solution.

A dynamic evaluation technique for assessing gas output from coal seams during commingling production within a coalbed methane well: a case study from the Qinshui Basin

Gas drainage is carried out based on output from each coal bed throughout commingling production of coalbed methane(CBM).A reasonable drainage process should therefore initially guarantee main coal bed production and then enhance gas output from other beds.Permanent damage can result if this is not the case,especially with regard to fracture development in the main gas-producing coal bed and can greatly reduce single well output.Current theoretical models and measuring devices are inapplicable to commingled CBM drainage,however,and so large errors in predictive models cannot always be avoided.The most effective currently available method involves directly measuring gas output from each coal bed as well as determining the dominant gas-producing unit.A dynamic evaluation technique for gas output from each coal bed during commingling CBM production is therefore proposed in this study.This technique comprises a downhole measurement system combined with a theoretical calculation model.Gas output parameters(i.e.,gas-phase flow rate,temperature,pressure)are measured in this approach via a downhole measurement system;substituting these parameters into a deduced theoretical calculation model then means that gas output from each seam can be calculated to determine the main gas-producing unit.Trends in gas output from a single well or each seam can therefore be predicted.The laboratory and field test results presented here demonstrate that calculation errors in CBM outputs can be controlled within a margin of 15%and therefore conform with field use requirements.

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.

Evaluation of the dynamic sealing performance of cap rocks of underground gas storage under multi-cycle alternating loads

The static sealing of underground gas storage(UGS),including the integrity of cap rocks and the stability of faults,is analyzed from a macro perspective using a comprehensive geological evaluation method.Changes in pore structure,permeability,and mechanical strength of cap rocks under cyclic loads may impact the rock sealing integrity during the injection and recovery phases of UGS.In this work,the mechanical deformation and failure tests of rocks,as well as rock damage tests under alternating loads,are conducted to analyze the changes in the strength and permeability of rocks under multiple-cycle intense injection and recovery of UGS.Additionally,this study proposes an evaluation method for the dynamic sealing performance of UGS cap rocks under multi-cycle alternating loads.The findings suggest that the failure strength(70%)can be used as the critical value for rock failure,thus providing theoretical support for determining the upper limit of operating pressure and the number of injection-recovery cycles for the safe operation of a UGS system.

Prediction of three-dimensional ocean temperature in the South China Sea based on time series gridded data and a dynamic spatiotemporal graph neural network

Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean temperature.Existing graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among data.In this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge.Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions.We also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data.In this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea surface.We compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.

Development and Application of a Production Data Analysis Model for a Shale Gas Production Well

This paper presents the development and application of a production data analysis software that can analyze and forecast the production performance and reservoir properties of shale gas wells.The theories used in the study were based on the analytical and empirical approaches.Its reliability has been confirmed through comparisons with a commercial software.Using transient data relating to multi-stage hydraulic fractured horizontal wells,it was confirmed that the accuracy of the modified hyperbolic method showed an error of approximately 4%compared to the actual estimated ultimate recovery(EUR).On the basis of the developed model,reliable productivity forecasts have been obtained by analyzing field production data relating to wells in Canada.The EUR was computed as 9.6 Bcf using the modified hyperbolic method.Employing the Pow Law Exponential method,the EUR would be 9.4 Bcf.The models developed in this study will allow in the future integration of new analytical and empirical theories in a relatively readily than commercial models.

Desired Dynamic Equation for Primary Frequency Modulation Control of Gas Turbines

Gas turbines play core roles in clean energy supply and the construction of comprehensive energy systems.The control performance of primary frequency modulation of gas turbines has a great impact on the frequency control of the power grid.However,there are some control difficulties in the primary frequency modulation control of gas turbines,such as the coupling effect of the fuel control loop and speed control loop,slow tracking speed,and so on.To relieve the abovementioned difficulties,a control strategy based on the desired dynamic equation proportional integral(DDE-PI)is proposed in this paper.Based on the parameter stability region,a parameter tuning procedure is summarized.Simulation is carried out to address the ease of use and simplicity of the proposed tuning method.Finally,DDE-PI is applied to the primary frequency modulation system of an MS6001B heavy-duty gas turbine.The simulation results indicate that the gas turbine with the proposed strategy can obtain the best control performance with a strong ability to deal with system uncertainties.The proposed method shows good engineering application potential.

Dynamic simulation of differential accumulation history of deep marine oil and gas in superimposed basin:A case study of Lower Paleozoic petroleum system of Tahe Oilfield,Tarim Basin,NW China

According to the complex differential accumulation history of deep marine oil and gas in superimposed basins,the Lower Paleozoic petroleum system in Tahe Oilfield of Tarim Basin is selected as a typical case,and the process of hydrocarbon generation and expulsion,migration and accumulation,adjustment and transformation of deep oil and gas is restored by means of reservoine-forming dynamics simulation.The thermal evolution history of the Lower Cambrian source rocks in Tahe Oilfield reflects the obvious differences in hydrocarbon generation and expulsion process and intensity in different tectonic zones,which is the main reason controlling the differences in deep oil and gas phases.The complex transport system composed of strike-slip fault and unconformity,etc.controlled early migration and accumulation and late adjustment of deep oil and gas,while the Middle Cambrian gypsum-salt rock in inner carbonate platform prevented vertical migration and accumulation of deep oil and gas,resulting in an obvious"fault-controlled"feature of deep oil and gas,in which the low potential area superimposed by the NE-strike-slip fault zone and deep oil and gas migration was conducive to accumulation,and it is mainly beaded along the strike-slip fault zone in the northeast direction.The dynamic simulation of reservoir formation reveals that the spatio-temporal configuration of"source-fault-fracture-gypsum-preservation"controls the differential accumulation of deep oil and gas in Tahe Oilfield.The Ordovician has experienced the accumulation history of multiple periods of charging,vertical migration and accumulation,and lateral adjustment and transformation,and deep oil and gas have always been in the dynamic equilibrium of migration,accumulation and escape.The statistics of residual oil and gas show that the deep stratum of Tahe Oilfield still has exploration and development potential in the Ordovician Yingshan Formation and Penglaiba Formation,and the Middle and Upper Cambrian ultra-deep stratum has a certain oil and gas resource prospect.This study provides a reference for the dynamic quantitative evaluation of deep oil and gas in the Tarim Basin,and also provides a reference for the study of reservoir formation and evolution in carbonate reservoir of paleo-craton basin.

Efficient and Stable Perovskite Solar Cells and Modules Enabled by Tailoring Additive Distribution According to the Film Growth Dynamics

Gas quenching and vacuum quenching process are widely applied to accelerate solvent volatilization to induce nucleation of perovskites in blade-coating method.In this work,we found these two pre-crystallization processes lead to different order of crystallization dynamics within the perovskite thin film,resulting in the differences of additive distribution.We then tailor-designed an additive molecule named 1,3-bis(4-methoxyphenyl)thiourea to obtain films with fewer defects and holes at the buried interface,and prepared perovskite solar cells with a certified efficiency of 23.75%.Furthermore,this work also demonstrates an efficiency of 20.18%for the large-area perovskite solar module(PSM)with an aperture area of 60.84 cm^(2).The PSM possesses remarkable continuous operation stability for maximum power point tracking of T_(90)>1000 h in ambient air.

Dynamic Interaction Analysis of Coupled Axial-Torsional-Lateral Mechanical Vibrations in Rotary Drilling Systems

Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.

Correlation between per-well average dynamic reserves and initial absolute open flow potential(AOFP) for large gas fields in China and its application

Based on performance data of over 600 wells in 32 large gas fields of different types in China, the correlation is established between per-well average dynamic reserves( G) and average initial absolute open flow potential(IAOFq) of each field, and its connotation and applicability are further discussed through theoretical deduction. In log-log plot, G vs. IAOFq exhibit highly dependent linear trend, which implicates the compatibility between G and IAOFq attained through development optimization to reach the balance among annual flow capacity, maximum profits and certain production plateau, that is to match productivity with rate maintenance capacity. The correlation can be used as analogue in new gas field development planning to evaluate the minimum dynamic reserves which meet the requirement of stable and profitable production, and facilitate well pattern arrangement. It can also serve as criteria to appraise the effectiveness and infill drilling potential of well patterns for developed gas fields.

Dynamic data packing towards the optimization of QoC and QoS in networked control systems

A class of networked control systems is investigated whose communication network is shared with other applications. The design objective for such a system setting is not only the optimization of the control performance but also the efficient utilization of the communication resources. We observe that at a large time scale the data packet delay in the communication network is roughly varying piecewise constant, which is typically true for data networks like the Internet. Based on this observation, a dynamic data packing scheme is proposed within the recently developed packet-based control framework for networked control systems. As expected this proposed approach achieves a fine balance between the control performance and the communication utilization: the similar control performance can be obtained at dramatically reduced cost of the communication resources. Simulations illustrate the effectiveness of the proposed approach.

Model of gas exchange dynamics for modified-atmosphere packages containing fresh produce

A model for modified-atmosphere packaging （MAP） systems containing fruits and vegetables was developed.The computer simulation was performed to predict the gas mass concentrations inside the packages and was successfully verified by experiments with yellow peaches at 5,15 and 25 ℃ using two types of packaging films.A Michaelis-Menten type respiration model with noncompetitive inhibition mechanism due to CO2 was adopted while the respiration rates were measured with an improved permeable system method suitable for either steady or unsteady state.The applicability of the model in the design of MAP systems was demonstrated with a calculation to evaluate film specification and equilibrium concentrations of O2 and CO2 in the package containing yellow peaches.

High-dimensional aerodynamic data modeling using a machine learning method based on a convolutional neural network

Modeling high-dimensional aerodynamic data presents a significant challenge in aero-loads prediction, aerodynamic shape optimization, flight control, and simulation. This article develops a machine learning approach based on a convolutional neural network (CNN) to address this problem. A CNN can implicitly distill features underlying the data. The number of parameters to be trained can be significantly reduced because of its local connectivity and parameter-sharing properties, which is favorable for solving high-dimensional problems in which the training cost can be prohibitive. A hypersonic wing similar to the Sanger aerospace plane carrier wing is employed as the test case to demonstrate the CNN-based modeling method. First, the wing is parameterized by the free-form deformation method, and 109 variables incorporating flight status and aerodynamic shape variables are defined as model input. Second, more than 7000 sample points generated by the Latin hypercube sampling method are evaluated by performing computational fluid dynamics simulations using a Reynolds-averaged Navier-Stokes flow solver to obtain an aerodynamic database, and a CNN model is built based on the observed data. Finally, the well-trained CNN model considering both flight status and shape variables is applied to aerodynamic shape optimization to demonstrate its capability to achieve fast optimization at multiple flight statuses.