Model reduction of fractional impedance spectra for time–frequency analysis of batteries, fuel cells, and supercapacitors

Joint time–frequency analysis is an emerging method for interpreting the underlying physics in fuel cells,batteries,and supercapacitors.To increase the reliability of time–frequency analysis,a theoretical correlation between frequency-domain stationary analysis and time-domain transient analysis is urgently required.The present work formularizes a thorough model reduction of fractional impedance spectra for electrochemical energy devices involving not only the model reduction from fractional-order models to integer-order models and from high-to low-order RC circuits but also insight into the evolution of the characteristic time constants during the whole reduction process.The following work has been carried out:(i)the model-reduction theory is addressed for typical Warburg elements and RC circuits based on the continued fraction expansion theory and the response error minimization technique,respectively;(ii)the order effect on the model reduction of typical Warburg elements is quantitatively evaluated by time–frequency analysis;(iii)the results of time–frequency analysis are confirmed to be useful to determine the reduction order in terms of the kinetic information needed to be captured;and(iv)the results of time–frequency analysis are validated for the model reduction of fractional impedance spectra for lithium-ion batteries,supercapacitors,and solid oxide fuel cells.In turn,the numerical validation has demonstrated the powerful function of the joint time–frequency analysis.The thorough model reduction of fractional impedance spectra addressed in the present work not only clarifies the relationship between time-domain transient analysis and frequency-domain stationary analysis but also enhances the reliability of the joint time–frequency analysis for electrochemical energy devices.

Metal-N_(4) model single‐atom catalyst with electroneutral quadri‐pyridine macrocyclic ligand for CO_(2) electroreduction

Metal–N–C single‐atom catalysts,mostly prepared from pyrolysis of metalorganic precursors,are widely used in heterogeneous electrocatalysis.Since metal sites with diverse local structures coexist in this type of material and it is challenging to characterize the local structure,a reliable structure–property relationship is difficult to establish.Conjugated macrocyclic complexes adsorbed on carbon support are well‐defined models to mimic the singleatom catalysts.Metal–N_(4) site with four electroneutral pyridine‐type ligands embedded in a graphene layer is the most commonly proposed structure of the active site of single‐atom catalysts,but its molecular counterpart has not been reported.In this work,we synthesized the conjugated macrocyclic complexes with a metal center(Co,Fe,or Ni)coordinated with four electroneutral pyridinic ligands as model catalysts for CO_(2) electroreduction.For comparison,the complexes with anionic quadri‐pyridine macrocyclic ligand were also prepared.The Co complex with the electroneutral ligand expressed a turnover frequency of CO formation more than an order of magnitude higher than that of the Co complex with the anionic ligand.Constrained ab initio molecular dynamics simulations based on the well‐defined structures of the model catalysts indicate that the Co complex with the electroneutral ligand possesses a stronger ability to mediate electron transfer from carbon to CO_(2).

Exploring the contribution of oxygen reduction reaction to Mg corrosion by modeling assisted local analysis

Oxygen reduction reaction(ORR)has been disclosed in recent studies as a significant secondary cathodic process during magnesium corrosion.This work elaborates on the contribution of ORR to the total corrosion process of pure Mg at different impurity levels in NaCl electrolyte with the assistance of local techniques.A finite element based numerical model taking into account the contribution of ORR during the corrosion of the Mg test materials has been designed in this study considering the local oxygen concentration.Respective computational simulations were calibrated based on the experimental data and evaluated accordingly.Finally,the simultaneous monitoring of local concentration of H_(2) and O_(2),and the combined modeling study reveal the relation between ORR and hydrogen evolution reaction.

Process Optimization and Modeling by Response Surface Methodology of Nitrite Electro-Reduction by Ti/RuO2 + IrO2 Electrode

Electrochemical reduction is one of the most suitable methods for the treatment of highly nitrate-contaminated solutions. This work focuses on the optimization of parameters influencing the electrochemical denitrification of water by the Ti/RuO2 + IrO2 electrode. The methodological approach used consists in carrying out a series of electrolysis by scrutinizing the reaction selectivity according to the experimental conditions. For this study, the ions concentrations before and after electrolysis were determined by UV-vis absorption spectroscopy. The results of the process optimization showed that the electrochemical reduction ofis efficient at neutral pH after 120 mn of electrolysis at -100 mA. In contrast to works found in the literature, this study highlighted the process modeling that could open interesting perspectives to develop new treatment methods of polluted waters.

The semi-analytical modeling and vibration reduction analysis of the cylindrical shell with piezoelectric shunt damping patches

By considering electromechanical coupling, a unified dynamic model of the cylindrical shell with the piezoelectric shunt damping patch(PSDP) is created. The model is universal and can simulate the vibration characteristic of the shell under different states including the states in which PSDP cannot be connected, partially connected, and completely connected to the shunt circuit. The equivalent loss factor and elastic modulus with frequency dependence are proposed to consider the electrical damping effect of resistance shunt circuits. Moreover, the semi-analytical dynamic equation of the cylindrical shell with PSDP is derived by the Lagrange equation. An experimental test is carried out on the cylindrical shell with PSDP to verify the vibration suppression ability of PSDP on the cylindrical shell and the correctness of the proposed model. Furthermore, the parameter analysis shows that determining the appropriate resistance value in the shunt circuit can achieve a good vibration suppression effect.

Developing a GIS Audit Framework in the Context of Information Technology though a Reductive Model Approach

A GIS audit framework is necessary considering the diverse nature of GIS with regard to components, applications and industry. In practice, checklists are generated during the audit process based on specific objectives. There is no standardized list of items that can be used as a reference. The purpose of this study was to develop a GIS audit framework as a foundation for GIS audits. The framework provides that comprehensive approach to various GIS aspects during the audit process. The design builds on a developed conceptual framework where most significant categories of GIS audit parameters namely data quality, software utilization, GIS competency and procedures (work flows) were identified. The study adopted a reductive model approach to simplify the complexity associated with each category of GIS audit parameter. The resultant audit elements for each category are organized in a matrix that forms an integral part of the framework. The columns comprise audit goal, audit questions and audit subjects as indicators which are qualitatively measured. The rows comprise the parameters (data quality, software utilization, personnel competency and procedure (workflows)). To use the framework, an auditor only needs to create an audit checklist that consists of particular parameters and indicators from the framework depending on audit objective. As part of an on-going research, the next step will involve validating the framework through a mock testing process.

Experimental Study of Air Layer Drag Reduction with Bottom Cavity for A Bulk Carrier Ship Model

Air lubrication by means of a bottom cavity is a promising method for ship drag reduction. The characteristics of the bottom cavity are sensitive to the flow field around the ship hull and the effect of drag reduction, especially the depth of the bottom cavity. In this study, a ship model experiment of a bulk carrier is conducted in a towing tank using the method of air layer drag reduction (ALDR) with different bottom cavity depths. The shape of the air layer is observed, and the changes in resistance are measured. The model experiments produce results of approximately 20% for the total drag reduction at the ship design speed for a 25-mm cavity continuously supplied with air at Cq = 0.224 in calm water, and the air layer covers the whole cavity when the air flow rate is suitable. In a regular head wave, the air layer is easily broken and reduces the drag reduction rate in short waves, particularly when λ/Lw1 is close to one;however, it still has a good drag reduction effect in the long waves.

Acid deposition critical loads modeling for the simulation of sulfur exceedance and reduction in Guangdong, China

The current acid deposition critical loads in Guangdong, China were calculated using the PROFILE model with a 3 km × 3 km resolution. Calculations were carded out for critical loads of potential acidity, actual acidity, sulfur and nitrogen, with values in extents of 0-3.5, 0-14.0, 0-26.0 and 0-3.5 kmol/（hrnE.year）, respectively. These values were comparable to previously reported results and reflected the influences of vegetation and soil characteristics on the soil acid buffering capacity. Simulations of SO2 emission and sulfur deposition in this study showed that sulfur deposition core areas mirrored SO2 emission centers. The prediction of sulfur deposition after 20% and 40% reduction of SO2 emission suggested that the reduction of area sources contributed greatly to the decrease of sulfur deposition. Thus, abatement of area source emissions could be the primary way to mitigate sulfur deposition in Guangdong to meet both the provincial and national regulations of air pollution control.

Mathematical model of the direct reduction of dust composite pellets containing zinc and iron

Direct reduction of dust composite pellets containing zinc and iron was examined by simulating the conditions of actual production process of a rotary hearth furnace （RHF） in laboratory. A mathematical model was constructed to study the reduction kinetics of iron oxides and ZnO in the dust composite pellets. It was validated by comparing the calculated values with experimental results. The effects of furnace temperature, pellet radius, and pellet porosity on the reduction were investigated by the model. It is shown that furnace temperature has obvious influence on both of the reduction of iron oxides and ZnO, but the influence of pellet radius and porosity is much smaller. Model calculations suggest that both of the reduction of iron oxides and ZnO are under mixed control with interface reactions and Boudouard reaction in the early stage, but only with interface reactions in the later stage.

Micro-bubble Drag Reduction on a High Speed Vessel Model

Ship hull form of the underwater area strongly influences the resistance of the ship. The major factor in ship resistance is skin friction resistance. Bulbous bows, polymer paint, water repellent paint （highly water-repellent wall）, air injection, and specific roughness have been used by researchers as an attempt to obtain the resistance reduction and operation efficiency of ships. Micro-bubble injection is a promising technique for lowering frictional resistance. The injected air bubbles are supposed to somehow modify the energy inside the turbulent boundary layer and thereby lower the skin friction. The purpose of this study was to identify the effect of injected micro bubbles on a navy fast patrol boat （FPB） 57 m type model with the following main dimensions： L=2 450 ram, B=400 mm, and T=190 mm. The influence of the location of micro bubble injection and bubble velocity was also investigated. The ship model was pulled by an electric motor whose speed could be varied and adjusted. The ship model resistance was precisely measured by a load cell transducer. Comparison of ship resistance with and without micro-bubble injection was shown on a graph as a function of the drag coefficient and Froude number. It was shown that micro bubble injection behind the mid-ship is the best location to achieve the most effective drag reduction, and the drag reduction caused by the micro-bubbles can reach 6%-9%.

Modeling and Design of H-Infinity Controller for Piezoelectric Actuator LIPCA

We proposed a dynamic model identification and design of an H-Infinity （i.e.H） controller using a LightweightPiezo-Composite Actuator （LIPCA）.A second-order dynamic model was obtained by using input and output data, and applyingan identification algorithm.The identified model coincides well with the real LIPCA.To reduce the resonating mode that istypical of piezoelectric actuators, a notch filter was used.A feedback controller using the Hcontrol scheme was designed basedon the identified dynamic model; thus, the LIPCA can be easily used as an actuator for biomemetic applications such as artificialmuscles or macro/micro positioning in bioengineering.The control algorithm was implemented using a microprocessor, analogfilters, and power amplifying drivers.Our simulation and experimental results demonstrate that the proposed control algorithmworks well in real environment, providing robust performance and stability with uncertain disturbances.

Model-based Predictive Control for Spatially-distributed Systems Using Dimensional Reduction Models

In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems （SDSs）. First, the dimension reduction with principal component analysis （PCA） is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.

Slope analysis based on local strength reduction method and variable-modulus elasto-plastic model

Employing an ideal elasto-plastic model,the typically used strength reduction method reduced the strength of all soil elements of a slope.Therefore,this method was called the global strength reduction method(GSRM).However,the deformation field obtained by GSRM could not reflect the real deformation of a slope when the slope became unstable.For most slopes,failure occurs once the strength of some regional soil is sufficiently weakened; thus,the local strength reduction method(LSRM)was proposed to analyze slope stability.In contrast with GSRM,LSRM only reduces the strength of local soil,while the strength of other soil remains unchanged.Therefore,deformation by LSRM is more reasonable than that by GSRM.In addition,the accuracy of the slope's deformation depends on the constitutive model to a large degree,and the variable-modulus elasto-plastic model was thus adopted.This constitutive model was an improvement of the Duncan–Chang model,which modified soil's deformation modulus according to stress level,and it thus better reflected the plastic feature of soil.Most importantly,the parameters of the variable-modulus elasto-plastic model could be determined through in-situ tests,and parameters determination by plate loading test and pressuremeter test were introduced.Therefore,it is easy to put this model into practice.Finally,LSRM and the variable-modulus elasto-plastic model were used to analyze Egongdai ancient landslide.Safety factor,deformation field,and optimal reinforcement measures for Egongdai ancient landslide were obtained based on the proposed method.

A Real-time Cutting Model Based on Finite Element and Order Reduction

Telemedicine plays an important role in Corona Virus Disease 2019(COVID-19).The virtual surgery simulation system,as a key component in telemedicine,requires to compute in real-time.Therefore,this paper proposes a realtime cutting model based on finite element and order reduction method,which improves the computational speed and ensure the real-time performance.The proposed model uses the finite element model to construct a deformation model of the virtual lung.Meanwhile,a model order reduction method combining proper orthogonal decomposition and Galerkin projection is employed to reduce the amount of deformation computation.In addition,the cutting path is formed according to the collision intersection position of the surgical instrument and the lesion area of the virtual lung.Then,the Bezier curve is adopted to draw the incision outline after the virtual lung has been cut.Finally,the simulation system is set up on the PHANTOM OMNI force haptic feedback device to realize the cutting simulation of the virtual lung.Experimental results show that the proposed model can enhance the real-time performance of telemedicine,reduce the complexity of the cutting simulation and make the incision smoother and more natural.

Maintenance decision-making method for manufacturing system based on cost and arithmetic reduction of intensity model

A cost-based selective maintenance decision-making method was presented.The purpose of this method was to find an optimal choice of maintenance actions to be performed on a selected group of machines for manufacturing systems.The arithmetic reduction of intensity model was introduced to describe the influence on machine failure intensity by different maintenance actions (preventive maintenance,minimal repair and overhaul).In the meantime,a resolution algorithm combining the greedy heuristic rules with genetic algorithm was provided.Finally,a case study of the maintenance decision-making problem of automobile workshop was given.Furthermore,the case study demonstrates the practicability of this method.

Experimental and Modeling Study on de-NO_x Characteristics of Selective Non-catalytic Reduction in O_2/CO_2 Atmosphere

An experimental study of thermal de-NOx using NH3 as reductant in 02/C02 atmosphere with the effect of S02 and different additives was performed in a drop tube furnace. Results show that the optimum temperature win- dow is 841-1184 ℃, and the optimum reaction temperature is about 900 ℃ with a de-NOx efficiency of 95.4%. A certain amount of S02 has an inhibiting effect on NO reduction. The effect of additives, including Na2C03, C2H5OH and FeCI3, on NO reduction by NH3 is also explored. The addition of Na2CO3 and FeCI3 is useful to widen the tem- perature window and shift the reaction to lower temperature for the efficiency is increased from 30.5% to 74.0% and 67.4% respectively at 800 ℃. Qualitatively, the modeling results using a detailed kinetic modeling mecha- nism represent well most of the process features. The effect of Na2CO3, C2H5OH and FeCI3 addition can be reproduced well by the Na2C03, C2H5OH and Fe（CO）5 sub-mechanism respectively. The reaction mechanism analysis shows that the effects of these additives on NO reduction are achieved mainly by promoting the produc- tion of OH radicals at lower temperature.

Dimensionality Reduction Using Optimized Self-Organized Map Technique for Hyperspectral Image Classification

The high dimensionalhyperspectral image classification is a challenging task due to the spectral feature vectors.The high correlation between these features and the noises greatly affects the classification performances.To overcome this,dimensionality reduction techniques are widely used.Traditional image processing applications recently propose numerous deep learning models.However,in hyperspectral image classification,the features of deep learning models are less explored.Thus,for efficient hyperspectral image classification,a depth-wise convolutional neural network is presented in this research work.To handle the dimensionality issue in the classification process,an optimized self-organized map model is employed using a water strider optimization algorithm.The network parameters of the self-organized map are optimized by the water strider optimization which reduces the dimensionality issues and enhances the classification performances.Standard datasets such as Indian Pines and the University of Pavia(UP)are considered for experimental analysis.Existing dimensionality reduction methods like Enhanced Hybrid-Graph Discriminant Learning(EHGDL),local geometric structure Fisher analysis(LGSFA),Discriminant Hyper-Laplacian projection(DHLP),Group-based tensor model(GBTM),and Lower rank tensor approximation(LRTA)methods are compared with proposed optimized SOM model.Results confirm the superior performance of the proposed model of 98.22%accuracy for the Indian pines dataset and 98.21%accuracy for the University of Pavia dataset over the existing maximum likelihood classifier,and Support vector machine(SVM).

A Novel Big Data Storage Reduction Model for Drill Down Search

Multi-level searching is called Drill down search.Right now,no drill down search feature is available in the existing search engines like Google,Yahoo,Bing and Baidu.Drill down search is very much useful for the end user tofind the exact search results among the huge paginated search results.Higher level of drill down search with category based search feature leads to get the most accurate search results but it increases the number and size of thefile system.The purpose of this manuscript is to implement a big data storage reduction binaryfile system model for category based drill down search engine that offers fast multi-levelfiltering capability.The basic methodology of the proposed model stores the search engine data in the binaryfile system model.To verify the effectiveness of the proposedfile system model,5 million unique keyword data are stored into a binaryfile,thereby analysing the proposedfile system with efficiency.Some experimental results are also provided based on real data that show our storage model speed and superiority.Experiments demonstrated that ourfile system expansion ratio is constant and it reduces the disk storage space up to 30%with conventional database/file system and it also increases the search performance for any levels of search.To discuss deeply,the paper starts with the short introduction of drill down search followed by the discussion of important technologies used to implement big data storage reduction system in detail.

Application of Different Models for the Prediction of the Kinetics of Direct Reduction of Natural Iron Ores

Simulation of the direct reduction conditions was performed in a laboratory furnace. Lump samples from natural hematite iron ore were reduced by a gas mixture of H2 and CO (H2/CO =1.5) at temperatures of 700°C, 800°C and 900°C. The effect of reduction temperature on the reduction degree, reduction rate of samples and carbon deposition were investigated and discussed in this study. The thermo-gravimetric data obtained from the reduction experiments was run in a programme that calculates the solid conversion rate. Also, three models: 1) Grain Model (GM), 2) Volumetric Model (VM), and 3) the Random Pore Model (RPM), were used to estimate the reduction kinetics of natural iron ores. It was found that the RPM model result agreed best with the obtained experimental results. Furthermore, it gave better predictions of the natural iron oxide conversion and thereby the reduction kinetics.

A Fatigue Damage Model for FRP Composite Laminate Systems Based on Stiffness Reduction

This paper introduces a stiffness reduction based model developed by the authors to characterize accumulative fatigue damage in unidirectional plies and(0/θ/0)composite laminates in fiber reinforced polymer(FRP)composite laminates.The proposed damage detection model is developed based on a damage evolution mechanism,including crack initiation and crack damage progress in matrix,matrix-fiber interface and fibers.Research result demonstrates that the corresponding stiffness of unidirectional composite laminates is reduced as the number of loading cycles progresses.First,three common models in literatures are presented and compared.Tensile viscosity,Young’s modulus and ultimate tensile stress of composites are incorporated as key factors in this model and are modified in accordance with temperature.Four types of FRP composite property parameters,including Carbon Fiber Reinforced Polymer(CFRP),Aramid Fiber Reinforced Polymer(AFRP),Glass Fiber Reinforced Polymer(GFRP),and Basalt Fiber Reinforced Polymer(BFRP),are considered in this research,and a comparative parameter study of FRP unidirectional composite laminates with different off-angle plies using control variate method are discussed.It is concluded that the relationship between the drop in stiffness and the number of cycles also shows three different regions,following the mechanism of damage of FRP composites and the matrix is the dominant factor determined by temperature,while fiber strength is the dominant factor that determine the reliability of composite.