Insights and reviews on battery lifetime prediction from research to practice

The rising demand for energy storage solutions,especially in the electric vehicle and renewable energy sectors,highlights the importance of accurately predicting battery health to enhance their longevity and reliability.This article comprehensively examines various methods used to forecast battery health,including physics-based models,empirical models,and equivalent circuit models,among others.It delves into the promise of data-driven prognostics,utilizing both conventional machine learning and cuttingedge deep neural network techniques.The advantages and limitations of hybrid models are thoroughly analyzed,with a focus on the benefits of integrating diverse data sources to improve prognostic precision.Through practical case studies,the article showcases the effectiveness and flexibility of these approaches.It also critically addresses the challenges encountered in applying battery health prognostics in realworld scenarios,such as issues of scalability,complexity,and data anomalies.Despite these challenges,the article underscores the emerging opportunities brought about by recent technological,academic,and research advancements.These include the development of digital twin models for batteries,the use of data-centric AI and standardized benchmarking,the potential integration of blockchain technology for enhanced data security and transparency,and the synergy between edge and cloud computing to boost data analysis and processing.The primary goal of this article is to enrich the understanding of current battery health prognostic techniques and to inspire further research aimed at overcoming existing hurdles and tapping into new opportunities.It concludes with a visionary perspective on future research directions and potential developments in this evolving field,encouraging both researchers and practitioners to explore innovative solutions.

Durability evaluation for existing railway engineering: a review

Purpose – This study aims to analyze the factors, evaluation techniques of the durability of existing railwayengineering.Design/methodology/approach – China has built a railway network of over 150,000 km. Ensuring thesafety of the existing railway engineering is of great significance for maintaining normal railway operationorder. However, railway engineering is a strip structure that crosses multiple complex environments. Andrailway engineering will withstand high-frequency impact loads from trains. The above factors have led todifferences in the deterioration characteristics and maintenance strategies of railway engineering compared toconventional concrete structures. Therefore, it is very important to analyze the key factors that affect thedurability of railway structures and propose technologies for durability evaluation.Findings – The factors that affect the durability and reliability of railway engineering are mainly divided intothree categories: material factors, environmental factors and load factors. Among them, material factors alsoinclude influencing factors, such as raw materials, mix proportions and so on. Environmental factors varydepending on the service environment of railway engineering, and the durability and deterioration of concretehave different failure mechanisms. Load factors include static load and train dynamic load. The on-site rapiddetection methods for five common diseases in railway engineering are also proposed in this paper. Thesemethods can quickly evaluate the durability of existing railway engineering concrete.Originality/value – The research can provide some new evaluation techniques and methods for thedurability of existing railway engineering.

Research on the solid particle erosion wear of pipe steel for hydraulic fracturing based on experiments and numerical simulations

Erosion wear is a common failure mode in the oil and gas industry.In the hydraulic fracturing,the fracturing pipes are not only in high-pressure working environment,but also suffer from the impact of the high-speed solid particles in the fracturing fluid.Beneath such complex conditions,the vulnerable components of the pipe system are prone to perforation or even burst accidents,which has become one of the most serious risks at the fracturing site.Unfortunately,it is not yet fully understood the erosion mechanism of pipe steel for hydraulic fracturing.Therefore,this article provides a detailed analysis of the erosion behavior of fracturing pipes under complex working conditions based on experiments and numerical simulations.Firstly,we conducted erosion experiments on AISI 4135 steel for fracturing pipes to investigate the erosion characteristics of the material.The effects of impact angle,flow velocity and applied stress on erosion wear were comprehensively considered.Then a particle impact dynamic model of erosion wear was developed based on the experimental parameters,and the evolution process of particle erosion under different impact angles,impact velocities and applied stress was analyzed.By combining the erosion characteristics,the micro-structure of the eroded area,and the micro-mechanics of erosion damage,the erosion mechanism of pipe steel under fracturing conditions was studied in detail for the first time.Under high-pressure operating conditions,it was demonstrated through experiments and numerical simulations that the size of the micro-defects in the eroded area increased as the applied stress increased,resulting in more severe erosion wear of fracturing pipes.

Current status and prospects of research on safety situation awareness of high speed railway operation environment

Purpose–The safety of high-speed rail operation environments is an important guarantee for the safe operation of high-speed rail.The operating environment of the high-speed rail is complex,and the main factors affecting the safety of high-speed rail operating environment include meteorological disasters,perimeter intrusion and external environmental hazards.The purpose of the paper is to elaborate on the current research status and team research progress on the perception of safety situation in high-speed rail operation environment and to propose directions for further research in the future.Design/methodology/approach–In terms of the mechanism and spatio-temporal evolution law of the main influencing factors on the safety of high-speed rail operation environments,the research status is elaborated,and the latest research progress and achievements of the team are introduced.This paper elaborates on the research status and introduces the latest research progress and achievements of the team in terms of meteorological,perimeter and external environmental situation perception methods for high-speed rail operation.Findings–Based on the technical route of“situational awareness evaluation warning active control,”a technical system for monitoring the safety of high-speed train operation environments has been formed.Relevant theoretical and technical research and application have been carried out around the impact of meteorological disasters,perimeter intrusion and the external environment on high-speed rail safety.These works strongly support the improvement of China’s railway environmental safety guarantee technology.Originality/value–With the operation of CR450 high-speed trains with a speed of 400 kmper hour and the application of high-speed train autonomous driving technology in the future,new and higher requirements have been put forward for the safety of high-speed rail operation environments.The following five aspects of work are urgently needed:(1)Research the single factor disaster mechanism of wind,rain,snow,lightning,etc.for high-speed railways with a speed of 400 kms per hour,and based on this,study the evolution characteristics of multiple safety factors and the correlation between the high-speed driving safety environment,revealing the coupling disastermechanism ofmultiple influencing factors;(2)Research covers multi-source data fusion methods and associated features such as disaster monitoring data,meteorological information,route characteristics and terrain and landforms,studying the spatio-temporal evolution laws of meteorological disasters,perimeter intrusions and external environmental hazards;(3)In terms of meteorological disaster situation awareness,research high-precision prediction methods for meteorological information time series along high-speed rail lines and study the realization of small-scale real-time dynamic and accurate prediction of meteorological disasters along high-speed rail lines;(4)In terms of perimeter intrusion,research amulti-modal fusion perception method for typical scenarios of high-speed rail operation in all time,all weather and all coverage and combine artificial intelligence technology to achieve comprehensive and accurate perception of perimeter security risks along the high-speed rail line and(5)In terms of external environment,based on the existing general network framework for change detection,we will carry out research on change detection and algorithms in the surrounding environment of highspeed rail.

Research on Modulation Signal Denoising Method Based on Improved Variational Mode Decomposition

In order to further analyze the micro-motion modulation signals generated by rotating components and extract micro-motion features,a modulation signal denoising algorithm based on improved variational mode decomposition(VMD)is proposed.To improve the time-frequency performance,this method decomposes the data into narrowband signals and analyzes the internal energy and frequency variations within the signal.Genetic algorithms are used to adaptively optimize the mode number and bandwidth control parameters in the process of VMD.This approach aims to obtain the optimal parameter combination and perform mode decomposition on the micro-motion modulation signal.The optimal mode number and quadratic penalty factor for VMD are determined.Based on the optimal values of the mode number and quadratic penalty factor,the original signal is decomposed using VMD,resulting in optimal mode number intrinsic mode function(IMF)components.The effective modes are then reconstructed with the denoised modes,achieving signal denoising.Through experimental data verification,the proposed algorithm demonstrates effective denoising of modulation signals.In simulation data validation,the algorithm achieves the highest signal-to-noise ratio(SNR)and exhibits the best performance.

Research on the dynamic response of connecting rod bearing bush wear of reciprocating machine under variable working conditions

As a type of reciprocating machine, the reciprocating compressor has a compact structure and many excitation sources.Once the small end bearing of the connecting rod is worn, it is easy to cause the sintering of the bearing and the abnormal vibration of the body.Based on the characteristics of poor lubrication state and complex force of connecting rod small head bearing, a mixed lubrication model considering oil groove feed was established, and the dynamic simulation of the reciprocating compressor model with lubricated bearings was carried out;considering different speeds and gas load conditions, the law of the impact of the eigenvalues changing with working conditions was explored.The fault simulation experiment was carried out by selecting representative working conditions, which verified the correctness of the simulation method.The study found that two contact collisions between the pin and the bearing bush occurred in one cycle, the collision impact was more severe under the wear fault, and the existence of the gap made the dynamic response more sensitive to the change of working conditions.This research provides ideas for the location and feature extraction of fault symptom signal angular segments in the process of complex measured signal processing.

Research on mechanical wear life feature fusion prediction method based on temporal pattern attention mechanism

In order to solve the problem of low prediction accuracy when only vibration or oil signal is used to predict the remaining life of gear wear,a gear wear life feature fusion prediction method based on temporal pattern attention mechanism is proposed.Firstly,deep residual shrinkage network(DRSN)is used to extract the features of the original vibration time series signals with low signal-tonoise ratio,and the vibration features associated with gear wear evolution are obtained.Secondly,the extracted vibration features and the oil monitoring data that can intuitively reflect the wear process information are jointly input into the bi-directional long short-term memory neural network based on temporal pattern attention mechanism(TPA-BiLSTM),the complex nonlinear relationship between vibration features,oil features and gear wear process evolution is further explored to improve the prediction accuracy.The gear life cycle dynamic response and wear process signals are obtained based on the gear numerical simulation model,and the feasibility of the proposed method is verified.Finally,the proposed method is applied to the residual life prediction of gear on a test bench,and the comparison between different methods proved the validity of the proposed method.

Research on the Corrosion of J55 Steel Due to Oxygen-Reducing Air Flooding in Low-Permeability Reservoirs

Oxygen-reducing air flooding is a low-permeability reservoir recovery technology with safety and low-cost advantages.However,in the process of air injection and drive,carbon in the air is oxidized through the crude oil reservoir to generate CO_(2),and this can cause serious corrosion in the recovery well.In this study,experiments on the corrosion of J55 tubular steel in a fluid environment with coexisting O_(2)and CO_(2)in an autoclave are presented.In particular,a weight loss method and a 3D morphometer were used to determine the average and the local corrosion rate.The corrosion surface morphology and composition were also measured by means of scanning electron microscopy(SEM)and an X-ray diffractometer(XRD).The corrosion pattern and morphological characteristics of J55 steel were analyzed in the O2/CO_(2)environment for different degrees of oxygen-reduction.As made evident by the experimental results,the corrosion products were mainly ferrous carbonate and iron oxide.In general,air injection increases the degree of oxygen reduction,from oxygen corrosion characteristics to CO_(2)corrosion-based characteristics.As a result,the corrosion product film becomes denser,and the corrosion rate is lower.

Research on heavy-haul adaptive technology and strengthening measures for existing railway steel bridge

Purpose–This research addresses the diverse characteristics of existing railway steel bridges in China,including variations in construction age,design standards,structural types,manufacturing processes,materials and service conditions.It also focuses on prominent defects and challenges related to heavy transportation conditions,particularly low live haul reserves and severe fatigue problems.Design/methodology/approach–The study encompasses three key aspects:(1)Adaptability assessment:It begins with assessing the suitability of existing railway steel bridges for heavy-haul operations through comprehensive analyses,experiments and engineering applications.(2)Strengthening:To combat frequent crack defects in the vertical stiffener end structure of girder webs,fatigue performance tests and reinforcement scheme experiments were conducted.These experiments included the development of a hot-spot stress S-N curve for this structure,validating the effectiveness of methods like crack stop holes,ultrasonic hammering and flange angle steel.(3)Service life extension:Research on the cruciform welded joint structure(non-fusion transfer type)focused on fatigue performance over the long life cycle.This led to the establishment of a fatigue S-N curve,enhancing Chinese design codes.Findings–The research achieved several significant outcomes:(1)Successful implementation of strengthening and retrofitting measures on a 64-m single-span double-track railway steel truss girder on an existing heavy-duty line.(2)Post-reinforcement,a substantial 26%to 32%reduction in live haul stress on bridge members was achieved.(3)The strengthening and retrofitting efforts met design expectations,enabling the bridge to accommodate vehicles with a 30-ton axle haul on the railway line.Originality/value–This research systematically tackles challenges and defects associated with Chinese existing railway steel bridges,providing valuable insights into adaptability assessment,strengthening techniques and service life extension methods.Furthermore,the development of fatigue S-N curves and the successful implementation of bridge enhancements have practical implications for improving the resilience and operational capacity of railway steel bridges in China.

Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization

Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based Bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in Bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.

Computer vision-aided DEM study on the compaction characteristics of graded subgrade filler considering realistic coarse particle shapes

The compaction quality of subgrade filler strongly affects subgrade settlement.The main objective of this research is to analyze the macro-and micro-mechanical compaction characteristics of subgrade filler based on the real shape of coarse particles.First,an improved Viola-Jones algorithm is employed to establish a digitalized 2D particle database for coarse particle shape evaluation and discrete modeling purposes of subgrade filler.Shape indexes of 2D subgrade filler are then computed and statistically analyzed.Finally,numerical simulations are performed to quantitatively investigate the effects of the aspect ratio(AR)and interparticle friction coefficient(μ)on the macro-and micro-mechanical compaction characteristics of subgrade filler based on the discrete element method(DEM).The results show that with the increasing AR,the coarse particles are narrower,leading to the increasing movement of fine particles during compaction,which indicates that it is difficult for slender coarse particles to inhibit the migration of fine particles.Moreover,the average displacement of particles is strongly influenced by the AR,indicating that their occlusion under power relies on particle shapes.The dis-placement and velocity of fine particles are much greater than those of the coarse particles,which shows that compaction is primarily a migration of fine particles.Under the cyclic load,the interparticle friction coefficientμhas little effect on the internal structure of the sample;under the quasi-static loads,however,the increase inμwill lead to a significant increase in the porosity of the sample.This study could not only provide a novel approach to investigate the compaction mechanism but also establish a new theoretical basis for the evaluation of intelligent subgrade compaction.

Genome-Wide Exploration of the Grape GLR Gene Family and Differential Responses of VvGLR3.1 and VvGLR3.2 to Low Temperature and Salt Stress

Grapes,one of the oldest tree species globally,are rich in vitamins.However,environmental conditions such as low temperature and soil salinization significantly affect grape yield and quality.The glutamate receptor(GLR)family,comprising highly conserved ligand-gated ion channels,regulates plant growth and development in response to stress.In this study,11 members of the VvGLR gene family in grapes were identified using whole-genome sequence analysis.Bioinformatic methods were employed to analyze the basic physical and chemical properties,phylogenetic trees,conserved domains,motifs,expression patterns,and evolutionary relationships.Phylogenetic and collinear analyses revealed that the VvGLRs were divided into three subgroups,showing the high conservation of the grape GLR family.These members exhibited 2 glutamate receptor binding regions(GABAb and GluR)and 3-4 transmembrane regions(M1,M2,M3,and M4).Real-time quantitative PCR analysis demonstrated the sensitivity of all VvGLRs to low temperature and salt stress.Subsequent localization studies in Nicotiana tabacum verified that VvGLR3.1 and VvGLR3.2 proteins were located on the cell membrane and cell nucleus.Additionally,yeast transformation experiments confirmed the functionality of VvGLR3.1 and VvGLR3.2 in response to low temperature and salt stress.Thesefindings highlight the significant role of the GLR family,a highly conserved group of ion channels,in enhancing grape stress resistance.This study offers new insights into the grape GLR gene family,providing fundamental knowledge for further functional analysis and breeding of stress-resistant grapevines.

A novel AgNPs/MOF substrate-based SERS sensor for high-sensitive on-site detection of wheat gluten

Gluten,known as the major allergen in wheat,has gained increasing concerns in industrialized countries,resulting in an urgent need for accurate,high-sensitive,and on-site detection of wheat gluten in complex food systems.Herein,we proposed a silver nanoparticles(AgNPs)/metal-organic framework(MOF)substrate-based surface-enhanced Raman scattering(SERS)sensor for the high-sensitive on-site detection of wheat gluten.The detection occurred on the newly in-situ synthesized AgNPs/MOF-modified SERS substrate,providing an enhancement factor(EF)of 1.89×10^(5).Benefitting from the signal amplification function of AgNPs/MOF and the superiority of SERS,this sensor represented high sensitivity performance and a wide detection range from 1×10^(-15)mol/L to 2×10^(-6)mol/L with a detection limit of 1.16×10^(-16)mol/L,which allowed monitoring the trace of wheat gluten in complex food system without matrix interference.This reliable sandwich SERS sensor may provide a promising platform for high-sensitive,accurate,and on-site detection of allergens in the field of food safety.

Stability-Considered Lane Keeping Control of Commercial Vehicles Based on Improved APF Algorithm

Regarding the lane keeping system,path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass,longer wheelbase and higher mass center.To improve the performance mentioned above comprehensively,the control strategy based on improved artificial potential field(APF)algorithm is proposed.In the paper,time to lane crossing(TLC)is introduced into the potential field function to enhance the accuracy of path tracking,meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source.The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory.In addition,adaptive inertial weight particle swarm optimization algorithm(AIWPSO)is applied to optimize the gain of each potential field function.The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably.Finally,the proposed control strategy is verified by the HiL test.It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving.

Nonlinear Characteristic Analysis of Gas-Interconnected Quasi-Zero Stiffness Pneumatic Suspension System:A Theoretical and Experimental Study

Because of significantly changed load and complex and variable driving road conditions of commercial vehicles,pneumatic suspension with lower natural frequencies is widely used in commercial vehicle suspension system.How ever,traditional pneumatic suspension system is hardly to respond the greatly changed load of commercial vehicles To address this issue,a new Gas-Interconnected Quasi-Zero Stiffness Pneumatic Suspension(GIQZSPS)is presented in this paper to improve the vibration isolation performance of commercial vehicle suspension systems under frequent load changes.This new structure adds negative stiffness air chambers on traditional pneumatic suspension to reduce the natural frequency of the suspension.It can adapt to different loads and road conditions by adjusting the solenoid valves between the negative stiffness air chambers.Firstly,a nonlinear mechanical model including the dimensionless stiffness characteristic and interconnected pipeline model is derived for GIQZSPS system.By the nonlinear mechanical model of GIQZSPS system,the force transmissibility rate is chosen as the evaluation index to analyze characteristics.Furthermore,a testing bench simulating 1/4 GIQZSPS system is designed,and the testing analysis of the model validation and isolating performance is carried out.The results show that compared to traditional pneumatic suspension,the GIQZSPS designed in the article has a lower natural frequency.And the system can achieve better vibration isolation performance under different load states by switching the solenoid valves between air chambers.

An Improved Bounded Conflict-Based Search for Multi-AGV Pathfinding in Automated Container Terminals

As the number of automated guided vehicles(AGVs)within automated container terminals(ACT)continues to rise,conflicts have becomemore frequent.Addressing point and edge conflicts ofAGVs,amulti-AGVconflict-free path planning model has been formulated to minimize the total path length of AGVs between shore bridges and yards.For larger terminalmaps and complex environments,the grid method is employed to model AGVs’road networks.An improved bounded conflict-based search(IBCBS)algorithmtailored to ACT is proposed,leveraging the binary tree principle to resolve conflicts and employing focal search to expand the search range.Comparative experiments involving 60 AGVs indicate a reduction in computing time by 37.397%to 64.06%while maintaining the over cost within 1.019%.Numerical experiments validate the proposed algorithm’s efficacy in enhancing efficiency and ensuring solution quality.

Simulation of Two-Phase Flowback Phenomena in Shale Gas Wells

The gas-water two-phaseflow occurring as a result of fracturingfluidflowback phenomena is known to impact significantly the productivity of shale gas well.In this work,this two-phaseflow has been simulated in the framework of a hybrid approach partially relying on the embedded discrete fracture model(EDFM).This model assumes the region outside the stimulated reservoir volume(SRV)as a single-medium while the SRV region itself is described using a double-medium strategy which can account for thefluid exchange between the matrix and the micro-fractures.The shale gas adsorption,desorption,diffusion,gas slippage effect,fracture stress sensitivity,and capillary imbibition have been considered.The shale gas production,pore pressure distribution and water saturation distribution in the reservoir have been simulated.The influences of hydraulic fracture geometry and nonorthogonal hydraulic fractures on gas production have been determined and discussed accordingly.The simulation results show that the daily gas production has an upward and downward trend due to the presence of a large amount of fracturingfluid in the reservoir around the hydraulic fracture.The smaller the angle between the hydraulic fracture and the wellbore,the faster the daily production of shale gas wells decreases,and the lower the cumulative production.Nonplanar fractures can increase the control volume of hydraulic fractures and improve the production of shale gas wells.

Indolocarbazole-Based Small Molecule Cathode-Active Material Exhibiting Double Redox for High-Voltage Li-Organic Batteries

Most organic electrode materials(OEMs)for rechargeable batteries employ n-type redox centers,whose redox potentials are intrinsically limited<3.0 V versus Li^(+)/Li.However,p-type materials possessing high redox potentials experience low specific capacities because they are capable of only a single redox reaction within the stable electrochemical window of typical electrolytes.Herein,we report 5,11-diethyl-5,11-dihydroindolo[3,2-b]carbazole(DEICZ)as a novel p-type OEM,exhibiting stable plateaus at high discharge potentials of 3.44 and 4.09 V versus Li^(+)/Li.Notably,the second redox potential of DEICZ is within the stable electrochemical window.The mechanism of the double redox reaction is investigated using both theoretical calculations and experimental measurements,including density functional theory calculations,ex situ electron spin resonance,and X-ray photoelectron spectroscopy.Finally,hybridization with single-walled carbon nanotubes(SWCNT)improves the cycle stability and rate performance of DEICZ owing to theπ-πinteractions between the SWCNT and co-planar molecular structure of DEICZ,preventing the dissolution of active materials into the electrolyte.The DEICZ/SWCNT composite electrode maintains 70.4%of its initial specific capacity at 1-C rate and also exhibits high-rate capability,even performing well at 100-C rate.Furthermore,we demonstrate its potential for flexible batteries after applying 1000 bending stresses to the composite electrode.

Surface morphologies of Mg-Gd alloy particle during its reactions with O_(2) and Teflon

Mg-Gd alloy particle has exhibited its unique combustion properties as the fuel of Mg/Teflon/Viton(MTV).Mg-Gd alloy/Teflon/Viton(MGTV)could burn at lower ambient pressure than MTV.To further investigate the reaction of MGTV in air,it was investigated via thermo gravity-differential scanning calorimetry(TG-DSC).Meanwhile,the morphologies and element distributions on the alloy surface during the reaction of MGTV in air were investigated via scanning electronic microscope-mapping-electronic differential spectrometer.Meanwhile,a similar experimental protocol on the Mg-Gd alloy particle during oxidation was also applied.The results showed that owning to a protective oxide shell,the onset oxidation temperature of Mg-Gd alloy is higher than Mg.However,the onset oxidation temperature of the exceeded Mg-Gd alloy in MGTV is significantly lower than that of the exceeded Mg in MTV.It was due to the existence of GdOF,which could significantly lower the oxidation temperature of the exceeded fuel.Furthermore,a possible reaction mechanism was proposed.The fascinating oxidation properties of Mg-Gd alloy suggested its promising applications in energetic materials.

Imaging simulation and analysis of attitude jitter effect on topographic mapping for lunar orbiter stereo optical cameras

The geometric accuracy of topographic mapping with high-resolution remote sensing images is inevita-bly affected by the orbiter attitude jitter.Therefore,it is necessary to conduct preliminary research on the stereo mapping camera equipped on lunar orbiter before launching.In this work,an imaging simulation method consid-ering the attitude jitter is presented.The impact analysis of different attitude jitter on terrain undulation is conduct-ed by simulating jitter at three attitude angles,respectively.The proposed simulation method is based on the rigor-ous sensor model,using the lunar digital elevation model(DEM)and orthoimage as reference data.The orbit and attitude of the lunar stereo mapping camera are simulated while considering the attitude jitter.Two-dimensional simulated stereo images are generated according to the position and attitude of the orbiter in a given orbit.Experi-mental analyses were conducted by the DEM with the simulated stereo image.The simulation imaging results demonstrate that the proposed method can ensure imaging efficiency without losing the accuracy of topographic mapping.The effect of attitude jitter on the stereo mapping accuracy of the simulated images was analyzed through a DEM comparison.