Aerodynamic instability evolution of a multi-stage combined compressor

Unstable operating conditions such as surge could cause damage to both aerodynamic performance and structural integrity of a compression system.This paper addresses the critical issue of aerodynamic instability in compressor design,particularly focusing on an axial-centrifugal combined compressor,a widely used yet underexplored configuration.An experimental investigation was conducted on a three-stage axial and one-stage centrifugal compressor(3A1C),using two pipe systems and employing fast-responding transducers to capture the dynamic instability process from choke condition to deep surge.Results reveal that at the design speed,3A1C enters deep surge directly,whereas at off-design speeds,it experiences rotating stall and mild surge across a wide mass flow range.Some special instability features in the combined compressor can be found in the steady state map and dynamic process.The characteristic curve of the first axial stage keeps a positive slope during the whole mass flow range at an off-design speed.The first stage could work stably on the stall characteristic curve because the centrifugal stage has stronger pressurization and plays a dominant role in global aerodynamic instability.Besides,rotating instability occurs at the first rotor tip and disappears as the back pressure increases,which is also rarely seen in a single-axial compressor.This is also related to the strong pressurization of the centrifugal stage.The findings of this paper will contribute to the understanding of aerodynamic instabilities in combined compressors.

Large-scale laboratory investigation of pillar-support interaction

Underground mine pillars provide natural stability to the mine area,allowing safe operations for workers and machinery.Extensive prior research has been conducted to understand pillar failure mechanics and design safe pillar layouts.However,limited studies(mostly based on empirical field observation and small-scale laboratory tests)have considered pillar-support interactions under monotonic loading conditions for the design of pillar-support systems.This study used a series of large-scale laboratory compression tests on porous limestone blocks to analyze rock and support behavior at a sufficiently large scale(specimens with edge length of 0.5 m)for incorporation of actual support elements,with consideration of different w/h ratios.Both unsupported and supported(grouted rebar rockbolt and wire mesh)tests were conducted,and the surface deformations of the specimens were monitored using three-dimensional(3D)digital image correlation(DIC).Rockbolts instrumented with distributed fiber optic strain sensors were used to study rockbolt strain distribution,load mobilization,and localized deformation at different w/h ratios.Both axial and bending strains were observed in the rockbolts,which became more prominent in the post-peak region of the stress-strain curve.

Experimental investigations on combined high and low cycle fatigue:Material-level specimen design and strain response characteristics

The paper designs a novel material-level specimen and its dedicated fixture suitable for applying Combined high-and low-Cycle Fatigue(CCF)loads.Unlike full-scale or simulation specimens,the CCF specimen eliminates geometrically induced stress gradients in the test region.Experimental data on CCF life and strain responses of ZSGH4169 alloy are acquired under different CCF loads.The Maximum Strain within Each(MSE)CCF cycle is demonstrated to be independent of the Low-Cycle Fatigue(LCF)loads and High-Cycle Fatigue(HCF)stress amplitudes,but exhibits a correlation with the Cycle Ratio of HCF/LCF(Rf).The growth law of MSE changes from linear to logarithmic as Rfdecreases.Strain amplitudes in the dwell stage,observed unaffected by Rf,are quantified as a function of LCF nominal stresses and HCF stress amplitudes.However,under a defined CCF load,strain amplitudes in the dwell stage remain constant.Strain peaks in the dwell stage in a single CCF cycle decrease in a power function with increasing HCF cycles.

An Innovative Approach to Predicting Scour Depth Around Foundations Under Combined Waves and Currents in Large-Scale Tests Based on Small-Scale Tests

This study presents an innovative theoretical approach to predicting the scour depth around a foundation in large-scale model tests based on small-scale model tests under combined waves and currents.In the present approach,the hydrodynamic parameters were designed based on the Froude similitude criteria.To avoid the cohesive behavior,we scaled the sediment size based on the settling velocity similarity,i.e.,the suspended load similarity.Then,a series of different scale model tests was conducted to obtain the scour depth around the pile in combined waves and currents.The fitting formula of scour depth from the small-scale model tests was used to predict the results of large-scale tests.The accuracy of the present approach was validated by comparing the prediction values with experimental data of large-scale tests.Moreover,the correctness and accuracy of the present approach for foundations with complex shapes,e.g.,the tripod foundation,was further checked.The results indicated that the fitting line from small-scale model tests slightly overestimated the experimental data of large-scale model tests,and the errors can be accepted.In general,the present approach was applied to predict the maximum or equilibrium scour depth of the large-scale model tests around single piles and tripods.

Dynamic simulation analysis of molten salt reactor-coupled air-steam combined cycle power generation system

A nonlinear dynamic simulation model based on coordinated control of speed and flow rate for the molten salt reactor and combined cycle systems is proposed here to ensure the coordination and stability between the molten salt reactor and power system.This model considers the impact of thermal properties of fluid variation on accuracy and has been validated with Simulink.This study reveals the capability of the control system to compensate for anomalous situations and maintain shaft stability in the event of perturbations occurring in high-temperature molten salt tank outlet parameters.Meanwhile,the control system’s impact on the system’s dynamic characteristics under molten salt disturbance is also analyzed.The results reveal that after the disturbance occurs,the controlled system benefits from the action of the control,and the overshoot and disturbance amplitude are positively correlated,while the system power and frequency eventually return to the initial values.This simulation model provides a basis for utilizing molten salt reactors for power generation and maintaining grid stability.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

Grain Yield,Biomass Accumulation,and Leaf Photosynthetic Characteristics of Rice under Combined Salinity-Drought Stress

Simultaneous stresses of salinity and drought often coincide during rice-growing seasons in saline lands,primarily due to insufficient water resources and inadequate irrigation facilities.Consequently,combined salinity-drought stress poses a major threat to rice production.In this study,two salinity levels(NS,non-salinity;HS,high salinity)along with three drought treatments(CC,control condition;DJ,drought stress imposed at jointing;DH,drought stress imposed at heading)were performed to investigate their combined influences on leaf photosynthetic characteristics,biomass accumulation,and rice yield formation.Salinity,drought,and their combination led to a shortened growth period from heading to maturity,resulting in a reduced overall growth duration.Grain yield was reduced under both salinity and drought stress,with a more substantial reduction under the combined salinity-drought stress.The combined stress imposed at heading caused greater yield losses in rice compared with the stress imposed at jointing.Additionally,the combined salinity-drought stress induced greater decreases in shoot biomass accumulation from heading to maturity,as well as in shoot biomass and nonstructural carbohydrate(NSC)content in the stem at heading and maturity.However,it increased the harvest index and NSC remobilization reserve.Salinity and drought reduced the leaf area index and SPAD value of flag leaves and weakened the leaf photosynthetic characteristics as indicated by lower photosynthetic rates,transpiration rates,and stomatal conductance.These reductions were more pronounced under the combined stress.Salinity,drought,and especially their combination,decreased the activities of ascorbate peroxidase,catalase,and superoxide dismutase,while increasing the contents of malondialdehyde,hydrogen peroxide,and superoxide radical.Our results indicated a more significant yield loss in rice when subjected to combined salinity-drought stress.The individual and combined stresses of salinity and drought diminished antioxidant enzyme activities,inhibited leaf photosynthetic functions,accelerated leaf senescence,and subsequently lowered assimilate accumulation and grain yield.

Research on the navigation method of large-scale differential tail-control improvised guided munitions based on rotational speed constraints

In the process of launching guided projectile under the conventional system, it is difficult to effectively obtain the precise navigation parameters of the projectile in the high dynamic environment. Aiming at this problem, this paper describes a new system of guided ammunition based on tail spin reduction. After analyzing the mechanism of the ammunition's tail spin reduction, a navigation method of large scale difference tail control simple guided ammunition based on speed constraint is proposed. In this method,the corresponding navigation constraints can be carried out by combining the rotation speed state of the ammunition itself, and the optimal solution of navigation parameters during the flight of the missile can be obtained by Extended Kalman Filter(EKF). Finally, the performance of the proposed method was verified by the simulation environment, and the hardware-in-the-loop simulation test and flight test were carried out to verify the performance of the method in the real environment. The experimental results show that the proposed method can achieve the optimal estimation of navigation parameters for simple guided ammunition with large-scale difference tail control. Under the conditions of simulation test and hardware-in-loop simulation test, the position and velocity errors calculated by the method in this paper converged. Under the condition of flight test, the spatial average error calculated by the method described in this paper is 6.17 m, and the spatial error of the final landing point is 3.50 m.Through this method, the accurate acquisition of navigation parameters in the process of projectile launching is effectively realized.

Pressure stimulated current in progressive failure process of combined coal-rock under uniaxial compression:Response and mechanism

Effective monitoring of the structural health of combined coal-rock under complex geological conditions by pressure stimulated currents(PSCs)has great potential for the understanding of dynamic disasters in underground engineering.To reveal the effect of this way,the uniaxial compression experiments with PSC monitoring were conducted on three types of coal-rock combination samples with different strength combinations.The mechanism explanation of PSCs are investigated by resistivity test,atomic force microscopy(AFM)and computed tomography(CT)methods,and a PSC flow model based on progressive failure process is proposed.The influence of strength combinations on PSCs in the progressive failure process are emphasized.The results show the PSC responses between rock part,coal part and the two components are different,which are affected by multi-scale fracture characteristics and electrical properties.As the rock strength decreases,the progressive failure process changes obviously with the influence range of interface constraint effect decreasing,resulting in the different responses of PSC strength and direction in different parts to fracture behaviors.The PSC flow model is initially validated by the relationship between the accumulated charges of different parts.The results are expected to provide a new reference and method for mining design and roadway quality assessment.

Influence of static cartoons combined with dynamic virtual environments on preoperative anxiety of preschool-aged children undergoing surgery

BACKGROUND Preschoolers become anxious when they are about to undergo anesthesia and surgery,warranting the development of more appropriate and effective interventions.AIM To explore the effect of static cartoons combined with dynamic virtual environments on preoperative anxiety and anesthesia induction compliance in preschool-aged children undergoing surgery.METHODS One hundred and sixteen preschool-aged children were selected and assigned to the drug(n=37),intervention(n=40),and control(n=39)groups.All the children received routine preoperative checkups and nursing before being transferred to the preoperative preparation room on the day of the operation.The drug group received 0.5 mg/kg midazolam and the intervention group treatment consisting of static cartoons combined with dynamic virtual environments.The control group received no intervention.The modified Yale Preoperative Anxiety Scale was used to evaluate the children’s anxiety level on the day before surgery(T0),before leaving the preoperative preparation room(T1),when entering the operating room(T2),and at anesthesia induction(T3).Compliance during anesthesia induction(T3)was evaluated using the Induction Compliance Checklist(ICC).Changes in mean arterial pressure(MAP),heart rate(HR),and respiratory rate(RR)were also recorded at each time point.RESULTS The anxiety scores of the three groups increased variously at T1 and T2.At T3,both the drug and intervention groups had similar anxiety scores,both of which were lower than those in the control group.At T1 and T2,MAP,HR,and RR of the three groups increased.The drug and control groups had significantly higher MAP and RR than the intervention group at T2.At T3,the MAP,HR,and RR of the drug group decreased and were significantly lower than those in the control group but were comparable to those in the intervention group.Both the drug and intervention groups had similar ICC scores and duration of anesthesia induction(T3),both of which were higher than those of the control group.CONCLUSION Combining static cartoons with dynamic virtual environments as effective as medication,specifically midazolam,in reducing preoperative anxiety and fear in preschool-aged children.This approach also improve their compliance during anesthesia induction and helped maintain their stable vital signs.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

A Review on Combined Strategy of Non-invasive Brain Stimulation and Robotic Therapy

Stroke is a major cause of death and disability among adults in China,and an efficient rehabilitation strategy has been an urgent demand for post-stroke rehabilitation.The non-invasive brain stimulation(NBS)can modulate the excitability of the cerebral cortex and provide after-effects apart from immediate effects to regain extremity motor functions,whereas robotic therapy provides high-intensity and long-duration repetitive movements to stimulate the cerebral cortex backward.The combined strategy of the two techniques is widely regarded as a promising application for stroke patients with dyskinesia.Transcranial magnetic stimulation(TMS)and transcranial electrical stimulation(TES)are important methods of NBS.Their recovery principles,stimulation parameters,and clinical applications have been summarized.The combined treatments of rTMS/tDCS and robotic therapy are analyzed and discussed to overcome the application barriers of the two techniques.The future development trend and the key technical problems are expounded for the clinical applications.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Large-Scale Carbon Dioxide Storage in Salt Caverns:Evaluation of Operation,Safety,and Potential in China

Underground salt cavern CO_(2) storage(SCCS)offers the dual benefits of enabling extensive CO_(2) storage and facilitating the utilization of CO_(2) resources while contributing the regulation of the carbon market.Its economic and operational advantages over traditional carbon capture,utilization,and storage(CCUS)projects make SCCS a more cost-effective and flexible option.Despite the widespread use of salt caverns for storing various substances,differences exist between SCCS and traditional salt cavern energy storage in terms of gas-tightness,carbon injection,brine extraction control,long-term carbon storage stability,and site selection criteria.These distinctions stem from the unique phase change characteristics of CO_(2) and the application scenarios of SCCS.Therefore,targeted and forward-looking scientific research on SCCS is imperative.This paper introduces the implementation principles and application scenarios of SCCS,emphasizing its connections with carbon emissions,carbon utilization,and renewable energy peak shaving.It delves into the operational characteristics and economic advantages of SCCS compared with other CCUS methods,and addresses associated scientific challenges.In this paper,we establish a pressure equation for carbon injection and brine extraction,that considers the phase change characteristics of CO_(2),and we analyze the pressure during carbon injection.By comparing the viscosities of CO_(2) and other gases,SCCS’s excellent sealing performance is demonstrated.Building on this,we develop a long-term stability evaluation model and associated indices,which analyze the impact of the injection speed and minimum operating pressure on stability.Field countermeasures to ensure stability are proposed.Site selection criteria for SCCS are established,preliminary salt mine sites suitable for SCCS are identified in China,and an initial estimate of achievable carbon storage scale in China is made at over 51.8-77.7 million tons,utilizing only 20%-30%volume of abandoned salt caverns.This paper addresses key scientific and engineering challenges facing SCCS and determines crucial technical parameters,such as the operating pressure,burial depth,and storage scale,and it offers essential guidance for implementing SCCS projects in China.

Numerical Investigation of Combined Production of Natural Gas Hydrate and Conventional Gas

Natural gas hydrate(NGH)is generally produced and accumulated together with the underlying conventional gas.Therefore,optimizing the production technology of these two gases should be seen as a relevant way to effectively reduce the exploitation cost of the gas hydrate.In this study,three types of models accounting for the coexistence of these gases are considered.Type A considers the upper hydrate-bearing layer(HBL)adjacent to the lower conventional gas layer(CGL);with the Type B a permeable interlayer exists between the upper HBL and the lower CGL;with the type C there is an impermeable interlayer between the upper HBL and the lower CGL.The production performances associated with the above three models are calculated under different conditions,including only a depressurized HBL(only HBL DP);only a depressurized CGL(only CGL DP);and both the HBL and the CGL being depressurized(HBL+CGL DP).The results show that for Type A and Type B coexistence accumulation models,when only HBL or CGL is depressurized,the gas from the other layer will flow into the production layer due to the pressure difference between the two layers.In the coexistence accumulation model of type C,the cumulative gas production is much lower than that of Type A and Type B,regardless of whether only HBL DP,only CGL DP,or HBL+CGL DP are considered.This indicates that the impermeable interlayer restricts the cross-flow of gas between HBL and CGL.For three different coexistence accumulation models,CGL DP has the largest gas-to-water ratio.