The internal solitary wave(ISW)represents a frequent and severe oceanic dynamic phenomenon observed in the South China Sea,exposing marine structures to sudden loads.This paper examines the prediction model of interac...The internal solitary wave(ISW)represents a frequent and severe oceanic dynamic phenomenon observed in the South China Sea,exposing marine structures to sudden loads.This paper examines the prediction model of interaction loads between ISW and FPSO,accounting for varying attack angles and incorporating ISW theories.The research demonstrates that the horizontal and transverse forces on FPSO under internal solitary waves(ISWs)comprise wave pressure difference force and viscous force,while the vertical force primarily consists of vertical wave pressure difference force.The wave pressure difference force is determined using the Froude-Krylov equation.The viscous force is derived from the tangential particle velocity induced by ISW and the viscous coefficient.The viscous coefficient formula is obtained through regression analysis of experimental data with different ISW attack angles.The research reveals that the horizontal viscous coefficient C_(vx)decreases as Reynolds number(R_(e))increases,while the transverse viscous coefficient C_(vy)initially increases and subsequently decreases with the growth of the Keulegan-Carpenter number(KC).Moreover,changes in wave propagation direction significantly affect the extreme magnitudes of both horizontal and transverse forces,and simultaneously modify the transverse force orientation,while having minimal impact on the vertical force.Additionally,the forces increase with the ISW’s amplitude.For horizontal and transverse forces,a thinner upper fluid layer generates larger forces.Comparative analysis of experimental,numerical,and theoretical results indicates strong agreement between theoretical predictions and experimental and numerical outcomes.展开更多
To minimize the deviation of the predicted creep curves obtained under constant load conditions by the original θ projection model, a new modified version that can be expressed by ε = θ_1(1-e^(-θ2t)) +θ3 (e^(θ_...To minimize the deviation of the predicted creep curves obtained under constant load conditions by the original θ projection model, a new modified version that can be expressed by ε = θ_1(1-e^(-θ2t)) +θ3 (e^(θ_4e^θ5^εt)-1), was derived and experimentally validated in our last study. In the present study, the predictive capability of the modified θ projection model was investigated by comparing the simulated and experimentally determined creep curves of K465 and DZ125 superalloys over a range of temperatures and stresses. Furthermore, the linear relationship between creep temperature and initial stress was extended to the 5-parameter model. The results indicated that the modified model could be used as a creep life prediction method, as it described the creep curve shape and resulted in predictions that fall within a specified error interval. Meanwhile, this modified model provides a more accurate way of describing creep curves under constant load conditions. The limitations and future direction of the modified model were also discussed. In addition, this modified θ projection model shows great potential for the evaluation and assessment of the service safety of structural materials used in components governed by creep deformation.展开更多
Background: Low back pain is the most common spinal disorder among soldiers, and load carriage training(LCT) is considered the main cause. We aimed to investigate changes in the spine system of soldiers after LCT at h...Background: Low back pain is the most common spinal disorder among soldiers, and load carriage training(LCT) is considered the main cause. We aimed to investigate changes in the spine system of soldiers after LCT at high altitudes and the change trend of the lumbar spine and surrounding soft tissues under different load conditions.Methods: Magnetic resonance imaging scans of the lumbar spines of nine soldiers from plateau troops were collected and processed. We used ImageJ and Surgimap software to analyze changes in the lumbar paraspinal muscles, intervertebral discs(IVDs), intervertebral foramina, and curvature. Furthermore, the multiple linear regression equation for spine injury owing to LCT at high altitudes was established as the mathematical prediction model using SPSS Statistics version 23.0 software.Results: In the paraspinal muscles, the cross-sectional area(CSA) increased significantly from(9126.4±691.6) mm~2 to(9862.7±456.4) mm~2, and the functional CSA(FCSA) increased significantly from(8089.6±707.7) mm~2 to(8747.9±426.2) mm~2 after LCT(P<0.05);however, the FCSA/CSA was not significantly different. Regarding IVD, the total lumbar spine showed a decreasing trend after LCT with a significant difference(P<0.05). Regarding the lumbar intervertebral foramen, the percentage of the effective intervertebral foraminal area of L3/4 significantly decreased from 91.6%±2.0% to 88.1%±2.9%(P<0.05). For curvature, the lumbosacral angle after LCT(32.4°±6.8°) was significantly higher(P<0.05) than that before LCT(26.6°±5.3°), while the lumbar lordosis angle increased significantly from(24.0°±7.1°) to(30.6°±7.4°)(P<0.05). The linear regression equation of the change rate, ΔFCSA%=–0.718+23.085×load weight, was successfully established as a prediction model of spinal injury after LCT at high altitudes.Conclusion: The spinal system encountered increased muscle volume, muscle congestion, tissue edema, IVD compression, decreased effective intervertebral foramen area, and increased lumbar curvature after LCT, which revealed important pathophysiological mechanisms of lumbar spinal disorders in soldiers following short-term and high-load weight training. The injury prediction model of the spinal system confirmed that a load weight <60% of soldiers' weight cannot cause acute pathological injury after short-term LCT, providing a reference supporting the formulation of the load weight standard for LCT.展开更多
In the realm of engineering practice,various factors such as limited availability of measurement data and complex working conditions pose significant challenges to obtaining accurate load spectra.Thus,accurately predi...In the realm of engineering practice,various factors such as limited availability of measurement data and complex working conditions pose significant challenges to obtaining accurate load spectra.Thus,accurately predicting the fatigue life of structures becomes notably arduous.This paper proposed an approach to predict the fatigue life of structure based on the optimized load spectra,which is accurately estimated by an efficient hinging hyperplane neural network(EHH-NN)model.The construction of the EHH-NN model includes initial network generation and parameter optimization.Through the combination of working conditions design,multi-body dynamics analysis and structural static mechanics analysis,the simulated load spectra of the structure are obtained.The simulated load spectra are taken as the input variables for the optimized EHH-NN model,while the measurement load spectra are used as the output variables.The prediction results of case structure indicate that the optimized EHH-NN model can achieve the high-accuracy load spectra,in comparison with support vector machine(SVM),random forest(RF)model and back propagation(BP)neural network.The error rate between the prediction values and the measurement values of the optimized EHH-NN model is 4.61%.In the Cauchy-Lorentz distribution,the absolute error data of 92%with EHH-NN model appear in the intermediate range of±1.65%.Also,the fatigue life analysis is performed for the case structure,based on the accurately predicted load spectra.The fatigue life of the case structure is calculated based on the comparison between the measured and predicted load spectra,with an accuracy of 93.56%.This research proposes the optimized EHH-NN model can more accurately reflect the measurement load spectra,enabling precise calculation of fatigue life.Additionally,the optimized EHH-NN model provides reliability assessment for industrial engineering equipment.展开更多
Accurate prediction of server load is important to cloud systems for improving the resource utilization, reducing the energy consumption and guaranteeing the quality of service(QoS).This paper analyzes the features of...Accurate prediction of server load is important to cloud systems for improving the resource utilization, reducing the energy consumption and guaranteeing the quality of service(QoS).This paper analyzes the features of cloud server load and the advantages and disadvantages of typical server load prediction algorithms, integrates the cloud model(CM) and the Markov chain(MC) together to realize a new CM-MC algorithm, and then proposes a new server load prediction algorithm based on CM-MC for cloud systems. The algorithm utilizes the historical data sample training method of the cloud model, and utilizes the Markov prediction theory to obtain the membership degree vector, based on which the weighted sum of the predicted values is used for the cloud model. The experiments show that the proposed prediction algorithm has higher prediction accuracy than other typical server load prediction algorithms, especially if the data has significant volatility. The proposed server load prediction algorithm based on CM-MC is suitable for cloud systems, and can help to reduce the energy consumption of cloud data centers.展开更多
Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares （PLS） is proposed in this article, where PLS, the method for predicting a set of dependent var...Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares （PLS） is proposed in this article, where PLS, the method for predicting a set of dependent variables forming a large set of predictors, is used to model the dynamic evolution between the space points and the corresponding future points. The model can eliminate error accumulation with the common single-step local model algorithm~ and refrain from the high multi-collinearity problem in the reconstructed state space with the increase of embedding dimension. Simulation predictions are done on the Mackey-Glass chaotic time series with the model. The satisfying prediction accuracy is obtained and the model efficiency verified. In the experiments, the number of extracted components in PLS is set with cross-validation procedure.展开更多
Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,...Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,there are limited regression models capable of predicting the PD under multistress levels,and these models have regression limitations and generally fail to cover the complexity of UGM behaviour.Recent researches are focused on using new methods of computational intelligence systems to address the problems,such as artificial neural network(ANN).In this context,we aim to develop an artificial neural model to predict the PD of UGMs exposed to repeated loads.Extensive repeated load triaxial tests(RLTTs)were conducted on base and subbase materials locally available in Victoria,Australia to investigate the PD properties of the tested materials and to prepare the database of the neural networks.Specimens were prepared over different moisture contents and gradations to cover a wide testing matrix.The ANN model consists of one input layer with five neurons,one hidden layer with twelve neurons,and one output layer with one neuron.The five inputs were the number of load cycles,deviatoric stress,moisture content,coefficient of uniformity,and coefficient of curvature.The sensitivity analysis showed that the most important indicator that impacts PD is the number of load cycles with influence factor of 41%.It shows that the ANN method is rapid and efficient to predict the PD,which could be implemented in the Austroads pavement design method.展开更多
To tackle the problem of inaccurate short-term bus load prediction,especially during holidays,a Transformer-based scheme with tailored architectural enhancements is proposed.First,the input data are clustered to reduc...To tackle the problem of inaccurate short-term bus load prediction,especially during holidays,a Transformer-based scheme with tailored architectural enhancements is proposed.First,the input data are clustered to reduce complexity and capture inherent characteristics more effectively.Gated residual connections are then employed to selectively propagate salient features across layers,while an attention mechanism focuses on identifying prominent patterns in multivariate time-series data.Ultimately,a pre-trained structure is incorporated to reduce computational complexity.Experimental results based on extensive data show that the proposed scheme achieves improved prediction accuracy over comparative algorithms by at least 32.00%consistently across all buses evaluated,and the fitting effect of holiday load curves is outstanding.Meanwhile,the pre-trained structure drastically reduces the training time of the proposed algorithm by more than 65.75%.The proposed scheme can efficiently predict bus load results while enhancing robustness for holiday predictions,making it better adapted to real-world prediction scenarios.展开更多
This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power sys...This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power system to ensure frequency stability,real-time economic optimization,control of the system and optimal load dispatch from it.A generalized terminal penalty term was used,and the finite-time convergence of the system was guaranteed.The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system,which had two areas connected by an AC tie line.The simulation results demonstrated the effectiveness of the algorithm.展开更多
A continuous-time Model Predictive Controller was proposed using Kautz function in order to improve the performance of Load Frequency Control(LFC).A dynamic model of an interconnected power system was used for Model P...A continuous-time Model Predictive Controller was proposed using Kautz function in order to improve the performance of Load Frequency Control(LFC).A dynamic model of an interconnected power system was used for Model Predictive Controller(MPC)design.MPC predicts the future trajectory of the dynamic model by calculating the optimal closed loop feedback gain matrix.In this paper,the optimal closed loop feedback gain matrix was calculated using Kautz function.Being an Orthonormal Basis Function(OBF),Kautz function has an advantage of solving complex pole-based nonlinear system.Genetic Algorithm(GA)was applied to optimally tune the Kautz function-based MPC.A constraint based on phase plane analysis was implemented with the cost function in order to improve the robustness of the Kautz function-based MPC.The proposed method was simulated with three area interconnected power system and the efficiency of the proposed method was measured and exhibited by comparing with conventional Proportional and Integral(PI)controller and Linear Quadratic Regulation(LQR).展开更多
Nowadays,AC electronic loads with energy recovery are widely used in the testing of uninterruptible power supplies and power supply equipment.To tackle the problems of control difficulty,strategy complexity,and poor d...Nowadays,AC electronic loads with energy recovery are widely used in the testing of uninterruptible power supplies and power supply equipment.To tackle the problems of control difficulty,strategy complexity,and poor dynamic performance of AC electronic load with energy recovery of the conventional control strategy,a control strategy of AC electronic load with energy recovery based on Finite Control Set Model Predictive Control(FCSMPC)is developed.To further reduce the computation burden of the FCS-MPC,a simplified FCS-MPC with transforming the predicted variables and using sector to select expected state is proposed.Through simplified model and equivalent approximation analysis,the transfer function of the system is obtained,and the stability and robustness of the system are analyzed.The performance of the simplified FCS-MPC is compared with space vector control(SVPWM)and conventional FCS-MPC.The results show that the FCS-MPC method performs better dynamic response and this advantage is more obvious when simulating high power loads.The simplified FCS-MPC shows similar control performance to conventional FCS-MPC at less computation burden.The control performance of the system also shows better simulation results.展开更多
The growing demands on global infrastructure highlight the critical need for durable and efficient pavement systems,particularly under the stress of repetitive heavy traffic loads.The use of geosynthetics within the p...The growing demands on global infrastructure highlight the critical need for durable and efficient pavement systems,particularly under the stress of repetitive heavy traffic loads.The use of geosynthetics within the pavement structure increases the load-carrying capacity of unbound pavement layers by providing lateral restraint,improving vertical stress distribution,and enhancing bearing capacity.Such reinforcement typically aims to either improve the service life of pavements or achieve equivalent performance with a reduced granular cover.Previous and ongoing research quantifies geosynthetic performance in pavement reinforcement using various testing methods.Among these,laboratory model box tests subjected to cyclic loading are pivotal,as they closely replicate real-world traffic conditions.Hence,these studies are essential for understanding how geosynthetics distribute loads and enhance pavement durability.This facilitates the development of optimized geosynthetic design and installation practices,accelerating the loading process to simulate years of traffic wear in a shorter period.This review discusses the improved rutting resistance of unbound pavements reinforced with geosynthetic materials,specifically drawing on data from cyclic plate load tests conducted on laboratory model boxes,as highlighted in the literature.Key variables such as optimum geosynthetic placement,geosynthetic material properties,performance of different geosynthetic materials and the effects of aperture shape and size on rutting resistance are discussed.Furthermore,the review assesses various predictive rutting models,analysing their applicability and accuracy in forecasting the rutting performance of geosynthetic-reinforced unbound pavements.This comprehensive literature review aids pavement engineers and researchers,in guiding the selection and design of geosynthetics to optimize pavement durability and functionality under repetitive traffic loads.展开更多
Accurate prediction of electric vehicle(EV)charging loads is a foundational step in the establishment of expressway charging infrastructures.This study introduces an approach to enhance the precision of expressway EV ...Accurate prediction of electric vehicle(EV)charging loads is a foundational step in the establishment of expressway charging infrastructures.This study introduces an approach to enhance the precision of expressway EV charging load predictions.The method considers both the battery dynamic state-of-charge(SOC)and user charging decisions.Expressway network nodes were first extracted using the open Gaode Map API to establish a model that incorporates the expressway network and traffic flow fea-tures.A Gaussian mixture model is then employed to construct a SOC distribution model for mixed traffic flow.An innovative SOC dynamic translation model is then introduced to capture the dynamic characteristics of traffic flow SOC values.Based on this foun-dation,an EV charging decision model was developed which considers expressway node distinctions.EV travel characteristics are extracted from the NHTS2017 datasets to assist in constructing the model.Differentiated decision-making is achieved by utilizing improved Lognormal and Sigmoid functions.Finally,the proposed method is applied to a case study of the Lian-Huo expressway.An analysis of EV charging power converges with historical data and shows that the method accurately predicts the charging loads of EVs on expressways,thus revealing the efficacy of the proposed approach in predicting EV charging dynamics under expressway scenarios.展开更多
Estimating long-term creep deformation and life of materials is an effective way to ensure the service safety and to reduce the cost of long-term integrity evaluation of high temperature structural materials.Since the...Estimating long-term creep deformation and life of materials is an effective way to ensure the service safety and to reduce the cost of long-term integrity evaluation of high temperature structural materials.Since the 1980 s, the θ projection model has been widely used for predicting creep lives due to its ability to capture the characteristic transitions observed in creep curves obtained under constant true stress conditions. However, the creep rupture behavior under constant load or engineering stress conditions cannot be simulated accurately using this model because of the different stress states. In this paper, creep curves obtained under constant load conditions were analyzed using a modified θ projection model by considering the increase in true stress with creep deformation during the creep tests. This model is expressed as ε = θ_1(1-e^(-θ_2t)) + θ3 e^(θ_4e^θ5^εt)-1, and was validated using the creep curves of K465 and DZ125 superalloys tested at a range of temperatures and engineering stresses. Moreover, it was shown that the predictive capability of the modified θ projection model was significantly improved over the original one, as it reduces the prediction uncertainty from a range of 10% to 20% to below 5%. Meanwhile,it was shown that the model can be reasonably used for predicting constant stress creep conditions, when appropriate parameters are used. The prediction performance of the modified model will be discussed in another paper. The results of this study show great potential for the evaluation and assessment of the service safety of structural materials used in applications where designs are limited by creep deformation.展开更多
Semi-active landing gear can provide good performance of both landing impact and taxi situation,and has the ability for adapting to various ground conditions and operational conditions.A kind of Nonlinear Model Predic...Semi-active landing gear can provide good performance of both landing impact and taxi situation,and has the ability for adapting to various ground conditions and operational conditions.A kind of Nonlinear Model Predictive Control algorithm(NMPC)for semi-active landing gears is developed in this paper.The NMPC algorithm uses Genetic Algorithm(GA)as the optimization technique and chooses damping performance of landing gear at touch down to be the optimization object.The valve's rate and magnitude limitations are also considered in the controller's design.A simulation model is built for the semi-active landing gear's damping process at touchdown.Drop tests are carried out on an experimental passive landing gear systerm to validate the parameters of the simulation model.The result of numerical simulation shows that the isolation of impact load at touchdown can be significantly improved compared to other control algorithms.The strongly nonlinear dynamics of semi-active landing gear coupled with control valve's rate and magnitude limitations are handled well with the proposed controller.展开更多
The variation of fuel loads after a fire for three forest types, phododendron -Larix gmetinii forest, herb--Larix gmelinii forest and herb--Betula plalyphlla forest , in the northern forest area of Daxing’anling regi...The variation of fuel loads after a fire for three forest types, phododendron -Larix gmetinii forest, herb--Larix gmelinii forest and herb--Betula plalyphlla forest , in the northern forest area of Daxing’anling region was discussed. The dynamic models were developed by gray theory for estimating the fuels loads of arbor- shrub, herbs’ grass, litter, and semi-decomposed litter, inflamma ble fuel and the total fuels in each forest type. After a fire, the inflammabIe fuel loads in phododendron-- Larix gmelinii forest and in the herb- - Betula platyphlla fores was estimated at 10.958 t/hm2and 10.473 t/hm2 respectively’ by 13 years later. and that was 12.297 t/hm 2 in herb--Larix gmeliniiforest by 7 years later.. It was predicated that a big fire may occur after 10 years based on inflammable fuel biomass accumulated.展开更多
The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power grid.The three-phase unbalanced is closely related to power planning and load distribution.When the...The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power grid.The three-phase unbalanced is closely related to power planning and load distribution.When the unbalance occurs,the safe operation of the electrical equipment will be seriously jeopardized.This paper proposes a Hierarchical Temporal Memory(HTM)-based three-phase unbalance prediction model consisted by the encoder for binary coding,the spatial pooler for frequency pattern learning,the temporal pooler for pattern sequence learning,and the sparse distributed representations classifier for unbalance prediction.Following the feasibility of spatial-temporal streaming data analysis,we adopted this brain-liked neural network to a real-time prediction for power load.We applied the model in five cities(Tangshan,Langfang,Qinhuangdao,Chengde,Zhangjiakou)of north China.We experimented with the proposed model and Long Short-term Memory(LSTM)model and analyzed the predict results and real currents.The results show that the predictions conform to the reality;compared to LSTM,the HTM-based prediction model shows enhanced accuracy and stability.The prediction model could serve for the overload warning and the load planning to provide high-quality power grid operation.展开更多
We used an analytical high-level battery model to estimate the battery lifetime for a given load.The experimental results show that this model to predict battery lifetime under variable loads is more appropriate than ...We used an analytical high-level battery model to estimate the battery lifetime for a given load.The experimental results show that this model to predict battery lifetime under variable loads is more appropriate than that under constant loads.展开更多
Recent industrial explosions globally have intensified the focus in mechanical engineering on designing infras-tructure systems and networks capable of withstanding blast loading.Initially centered on high-profile fac...Recent industrial explosions globally have intensified the focus in mechanical engineering on designing infras-tructure systems and networks capable of withstanding blast loading.Initially centered on high-profile facilities such as embassies and petrochemical plants,this concern now extends to a wider array of infrastructures and facilities.Engineers and scholars increasingly prioritize structural safety against explosions,particularly to prevent disproportionate collapse and damage to nearby structures.Urbanization has further amplified the reliance on oil and gas pipelines,making them vital for urban life and prime targets for terrorist activities.Consequently,there is a growing imperative for computational engineering solutions to tackle blast loading on pipelines and mitigate associated risks to avert disasters.In this study,an empty pipe model was successfully validated under contact blast conditions using Abaqus software,a powerful tool in mechanical engineering for simulating blast effects on buried pipelines.Employing a Eulerian-Lagrangian computational fluid dynamics approach,the investigation extended to above-surface and below-surface blasts at standoff distances of 25 and 50 mm.Material descriptions in the numerical model relied on Abaqus’default mechanical models.Comparative analysis revealed varying pipe performance,with deformation decreasing as explosion-to-pipe distance increased.The explosion’s location relative to the pipe surface notably influenced deformation levels,a key finding highlighted in the study.Moreover,quantitative findings indicated varying ratios of plastic dissipation energy(PDE)for different blast scenarios compared to the contact blast(P0).Specifically,P1(25 mm subsurface blast)and P2(50 mm subsurface blast)showed approximately 24.07%and 14.77%of P0’s PDE,respectively,while P3(25 mm above-surface blast)and P4(50 mm above-surface blast)exhibited lower PDE values,accounting for about 18.08%and 9.67%of P0’s PDE,respectively.Utilising energy-absorbing materials such as thin coatings of ultra-high-strength concrete,metallic foams,carbon fiber-reinforced polymer wraps,and others on the pipeline to effectively mitigate blast damage is recommended.This research contributes to the advancement of mechanical engineering by providing insights and solutions crucial for enhancing the resilience and safety of underground pipelines in the face of blast events.展开更多
基金supported by JUST Start-up Fund for Science Research,the Jiangsu Natural Science Foundation(Grant No.BK20210885).
文摘The internal solitary wave(ISW)represents a frequent and severe oceanic dynamic phenomenon observed in the South China Sea,exposing marine structures to sudden loads.This paper examines the prediction model of interaction loads between ISW and FPSO,accounting for varying attack angles and incorporating ISW theories.The research demonstrates that the horizontal and transverse forces on FPSO under internal solitary waves(ISWs)comprise wave pressure difference force and viscous force,while the vertical force primarily consists of vertical wave pressure difference force.The wave pressure difference force is determined using the Froude-Krylov equation.The viscous force is derived from the tangential particle velocity induced by ISW and the viscous coefficient.The viscous coefficient formula is obtained through regression analysis of experimental data with different ISW attack angles.The research reveals that the horizontal viscous coefficient C_(vx)decreases as Reynolds number(R_(e))increases,while the transverse viscous coefficient C_(vy)initially increases and subsequently decreases with the growth of the Keulegan-Carpenter number(KC).Moreover,changes in wave propagation direction significantly affect the extreme magnitudes of both horizontal and transverse forces,and simultaneously modify the transverse force orientation,while having minimal impact on the vertical force.Additionally,the forces increase with the ISW’s amplitude.For horizontal and transverse forces,a thinner upper fluid layer generates larger forces.Comparative analysis of experimental,numerical,and theoretical results indicates strong agreement between theoretical predictions and experimental and numerical outcomes.
基金support provided by the National Key Research and Development Program of China (Grant No.2017YFB0702902)the National Natural Science Foundation of China (Grant Nos.51631008 and 51771019)the National High Technology Research Program of China (Grant No.2012AA03A513) as well as the 111 Project (No.B170003)
文摘To minimize the deviation of the predicted creep curves obtained under constant load conditions by the original θ projection model, a new modified version that can be expressed by ε = θ_1(1-e^(-θ2t)) +θ3 (e^(θ_4e^θ5^εt)-1), was derived and experimentally validated in our last study. In the present study, the predictive capability of the modified θ projection model was investigated by comparing the simulated and experimentally determined creep curves of K465 and DZ125 superalloys over a range of temperatures and stresses. Furthermore, the linear relationship between creep temperature and initial stress was extended to the 5-parameter model. The results indicated that the modified model could be used as a creep life prediction method, as it described the creep curve shape and resulted in predictions that fall within a specified error interval. Meanwhile, this modified model provides a more accurate way of describing creep curves under constant load conditions. The limitations and future direction of the modified model were also discussed. In addition, this modified θ projection model shows great potential for the evaluation and assessment of the service safety of structural materials used in components governed by creep deformation.
基金supported by the National Key Research and Development Program of China (2018YFB1307603)the National Natural Science Foundation of China (8174100706)。
文摘Background: Low back pain is the most common spinal disorder among soldiers, and load carriage training(LCT) is considered the main cause. We aimed to investigate changes in the spine system of soldiers after LCT at high altitudes and the change trend of the lumbar spine and surrounding soft tissues under different load conditions.Methods: Magnetic resonance imaging scans of the lumbar spines of nine soldiers from plateau troops were collected and processed. We used ImageJ and Surgimap software to analyze changes in the lumbar paraspinal muscles, intervertebral discs(IVDs), intervertebral foramina, and curvature. Furthermore, the multiple linear regression equation for spine injury owing to LCT at high altitudes was established as the mathematical prediction model using SPSS Statistics version 23.0 software.Results: In the paraspinal muscles, the cross-sectional area(CSA) increased significantly from(9126.4±691.6) mm~2 to(9862.7±456.4) mm~2, and the functional CSA(FCSA) increased significantly from(8089.6±707.7) mm~2 to(8747.9±426.2) mm~2 after LCT(P<0.05);however, the FCSA/CSA was not significantly different. Regarding IVD, the total lumbar spine showed a decreasing trend after LCT with a significant difference(P<0.05). Regarding the lumbar intervertebral foramen, the percentage of the effective intervertebral foraminal area of L3/4 significantly decreased from 91.6%±2.0% to 88.1%±2.9%(P<0.05). For curvature, the lumbosacral angle after LCT(32.4°±6.8°) was significantly higher(P<0.05) than that before LCT(26.6°±5.3°), while the lumbar lordosis angle increased significantly from(24.0°±7.1°) to(30.6°±7.4°)(P<0.05). The linear regression equation of the change rate, ΔFCSA%=–0.718+23.085×load weight, was successfully established as a prediction model of spinal injury after LCT at high altitudes.Conclusion: The spinal system encountered increased muscle volume, muscle congestion, tissue edema, IVD compression, decreased effective intervertebral foramen area, and increased lumbar curvature after LCT, which revealed important pathophysiological mechanisms of lumbar spinal disorders in soldiers following short-term and high-load weight training. The injury prediction model of the spinal system confirmed that a load weight <60% of soldiers' weight cannot cause acute pathological injury after short-term LCT, providing a reference supporting the formulation of the load weight standard for LCT.
基金Supported by National Natural Science Foundation of China(Grant No.51805447)Natural Science Foundation of Jiangsu Higher Education of China(Grant No.22KJB460010)+2 种基金Jiangsu Provincial Innovation and Promotion Project of Forestry Science and Technology of China(Grant No.LYKJ[2023]06)Yangzhou Science and Technology Plan(City School Cooperation Project)of China(Grant No.YZ2022193)Cyan Blue Project of Yangzhou University of China。
文摘In the realm of engineering practice,various factors such as limited availability of measurement data and complex working conditions pose significant challenges to obtaining accurate load spectra.Thus,accurately predicting the fatigue life of structures becomes notably arduous.This paper proposed an approach to predict the fatigue life of structure based on the optimized load spectra,which is accurately estimated by an efficient hinging hyperplane neural network(EHH-NN)model.The construction of the EHH-NN model includes initial network generation and parameter optimization.Through the combination of working conditions design,multi-body dynamics analysis and structural static mechanics analysis,the simulated load spectra of the structure are obtained.The simulated load spectra are taken as the input variables for the optimized EHH-NN model,while the measurement load spectra are used as the output variables.The prediction results of case structure indicate that the optimized EHH-NN model can achieve the high-accuracy load spectra,in comparison with support vector machine(SVM),random forest(RF)model and back propagation(BP)neural network.The error rate between the prediction values and the measurement values of the optimized EHH-NN model is 4.61%.In the Cauchy-Lorentz distribution,the absolute error data of 92%with EHH-NN model appear in the intermediate range of±1.65%.Also,the fatigue life analysis is performed for the case structure,based on the accurately predicted load spectra.The fatigue life of the case structure is calculated based on the comparison between the measured and predicted load spectra,with an accuracy of 93.56%.This research proposes the optimized EHH-NN model can more accurately reflect the measurement load spectra,enabling precise calculation of fatigue life.Additionally,the optimized EHH-NN model provides reliability assessment for industrial engineering equipment.
基金supported by National Natural Science Foundation of China(61533013,61273144)Scientific Technology Research and Development Plan Project of Tangshan(13130298B)Scientific Technology Research and Development Plan Project of Hebei(z2014070)
基金supported by the National Natural Science Foundation of China(61472192 61772286)+3 种基金the National Key Research and Development Program of China(2018YFB1003700)the Scientific and Technological Support Project(Society)of Jiangsu Province(BE2016776)the "333" Project of Jiangsu Province(BRA2017228 BRA2017401)
文摘Accurate prediction of server load is important to cloud systems for improving the resource utilization, reducing the energy consumption and guaranteeing the quality of service(QoS).This paper analyzes the features of cloud server load and the advantages and disadvantages of typical server load prediction algorithms, integrates the cloud model(CM) and the Markov chain(MC) together to realize a new CM-MC algorithm, and then proposes a new server load prediction algorithm based on CM-MC for cloud systems. The algorithm utilizes the historical data sample training method of the cloud model, and utilizes the Markov prediction theory to obtain the membership degree vector, based on which the weighted sum of the predicted values is used for the cloud model. The experiments show that the proposed prediction algorithm has higher prediction accuracy than other typical server load prediction algorithms, especially if the data has significant volatility. The proposed server load prediction algorithm based on CM-MC is suitable for cloud systems, and can help to reduce the energy consumption of cloud data centers.
文摘Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares （PLS） is proposed in this article, where PLS, the method for predicting a set of dependent variables forming a large set of predictors, is used to model the dynamic evolution between the space points and the corresponding future points. The model can eliminate error accumulation with the common single-step local model algorithm~ and refrain from the high multi-collinearity problem in the reconstructed state space with the increase of embedding dimension. Simulation predictions are done on the Mackey-Glass chaotic time series with the model. The satisfying prediction accuracy is obtained and the model efficiency verified. In the experiments, the number of extracted components in PLS is set with cross-validation procedure.
文摘Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,there are limited regression models capable of predicting the PD under multistress levels,and these models have regression limitations and generally fail to cover the complexity of UGM behaviour.Recent researches are focused on using new methods of computational intelligence systems to address the problems,such as artificial neural network(ANN).In this context,we aim to develop an artificial neural model to predict the PD of UGMs exposed to repeated loads.Extensive repeated load triaxial tests(RLTTs)were conducted on base and subbase materials locally available in Victoria,Australia to investigate the PD properties of the tested materials and to prepare the database of the neural networks.Specimens were prepared over different moisture contents and gradations to cover a wide testing matrix.The ANN model consists of one input layer with five neurons,one hidden layer with twelve neurons,and one output layer with one neuron.The five inputs were the number of load cycles,deviatoric stress,moisture content,coefficient of uniformity,and coefficient of curvature.The sensitivity analysis showed that the most important indicator that impacts PD is the number of load cycles with influence factor of 41%.It shows that the ANN method is rapid and efficient to predict the PD,which could be implemented in the Austroads pavement design method.
文摘To tackle the problem of inaccurate short-term bus load prediction,especially during holidays,a Transformer-based scheme with tailored architectural enhancements is proposed.First,the input data are clustered to reduce complexity and capture inherent characteristics more effectively.Gated residual connections are then employed to selectively propagate salient features across layers,while an attention mechanism focuses on identifying prominent patterns in multivariate time-series data.Ultimately,a pre-trained structure is incorporated to reduce computational complexity.Experimental results based on extensive data show that the proposed scheme achieves improved prediction accuracy over comparative algorithms by at least 32.00%consistently across all buses evaluated,and the fitting effect of holiday load curves is outstanding.Meanwhile,the pre-trained structure drastically reduces the training time of the proposed algorithm by more than 65.75%.The proposed scheme can efficiently predict bus load results while enhancing robustness for holiday predictions,making it better adapted to real-world prediction scenarios.
基金supported by the National Natural Science Foundation of China(Grant 62103101)the Natural Science Foundation of Jiangsu Province of China(Grant BK20210217)+5 种基金the China Postdoctoral Science Foundation(Grant 2022M710680)the National Natural Science Foundation of China(Grant 62273094)the"Zhishan"Scholars Programs of Southeast Universitythe Fundamental Science(Natural Science)General Program of Jiangsu Higher Education Institutions(No.21KJB470020)the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network,Nanjing Institute of Technology(No.XTCX202102)the Introduced Talents Scientific Research Start-up Fund Project,Nanjing Institute of Technology(No.YKJ202133).
文摘This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power system to ensure frequency stability,real-time economic optimization,control of the system and optimal load dispatch from it.A generalized terminal penalty term was used,and the finite-time convergence of the system was guaranteed.The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system,which had two areas connected by an AC tie line.The simulation results demonstrated the effectiveness of the algorithm.
文摘A continuous-time Model Predictive Controller was proposed using Kautz function in order to improve the performance of Load Frequency Control(LFC).A dynamic model of an interconnected power system was used for Model Predictive Controller(MPC)design.MPC predicts the future trajectory of the dynamic model by calculating the optimal closed loop feedback gain matrix.In this paper,the optimal closed loop feedback gain matrix was calculated using Kautz function.Being an Orthonormal Basis Function(OBF),Kautz function has an advantage of solving complex pole-based nonlinear system.Genetic Algorithm(GA)was applied to optimally tune the Kautz function-based MPC.A constraint based on phase plane analysis was implemented with the cost function in order to improve the robustness of the Kautz function-based MPC.The proposed method was simulated with three area interconnected power system and the efficiency of the proposed method was measured and exhibited by comparing with conventional Proportional and Integral(PI)controller and Linear Quadratic Regulation(LQR).
文摘Nowadays,AC electronic loads with energy recovery are widely used in the testing of uninterruptible power supplies and power supply equipment.To tackle the problems of control difficulty,strategy complexity,and poor dynamic performance of AC electronic load with energy recovery of the conventional control strategy,a control strategy of AC electronic load with energy recovery based on Finite Control Set Model Predictive Control(FCSMPC)is developed.To further reduce the computation burden of the FCS-MPC,a simplified FCS-MPC with transforming the predicted variables and using sector to select expected state is proposed.Through simplified model and equivalent approximation analysis,the transfer function of the system is obtained,and the stability and robustness of the system are analyzed.The performance of the simplified FCS-MPC is compared with space vector control(SVPWM)and conventional FCS-MPC.The results show that the FCS-MPC method performs better dynamic response and this advantage is more obvious when simulating high power loads.The simplified FCS-MPC shows similar control performance to conventional FCS-MPC at less computation burden.The control performance of the system also shows better simulation results.
基金financial and intellectual support provided by Queensland University of Technology(QUT)through its Higher Degree Research Program.
文摘The growing demands on global infrastructure highlight the critical need for durable and efficient pavement systems,particularly under the stress of repetitive heavy traffic loads.The use of geosynthetics within the pavement structure increases the load-carrying capacity of unbound pavement layers by providing lateral restraint,improving vertical stress distribution,and enhancing bearing capacity.Such reinforcement typically aims to either improve the service life of pavements or achieve equivalent performance with a reduced granular cover.Previous and ongoing research quantifies geosynthetic performance in pavement reinforcement using various testing methods.Among these,laboratory model box tests subjected to cyclic loading are pivotal,as they closely replicate real-world traffic conditions.Hence,these studies are essential for understanding how geosynthetics distribute loads and enhance pavement durability.This facilitates the development of optimized geosynthetic design and installation practices,accelerating the loading process to simulate years of traffic wear in a shorter period.This review discusses the improved rutting resistance of unbound pavements reinforced with geosynthetic materials,specifically drawing on data from cyclic plate load tests conducted on laboratory model boxes,as highlighted in the literature.Key variables such as optimum geosynthetic placement,geosynthetic material properties,performance of different geosynthetic materials and the effects of aperture shape and size on rutting resistance are discussed.Furthermore,the review assesses various predictive rutting models,analysing their applicability and accuracy in forecasting the rutting performance of geosynthetic-reinforced unbound pavements.This comprehensive literature review aids pavement engineers and researchers,in guiding the selection and design of geosynthetics to optimize pavement durability and functionality under repetitive traffic loads.
基金supported by the Unveiling and Leading Projects of Gansu Provincial Department of Transportation(JT-JJ-2023-008).
文摘Accurate prediction of electric vehicle(EV)charging loads is a foundational step in the establishment of expressway charging infrastructures.This study introduces an approach to enhance the precision of expressway EV charging load predictions.The method considers both the battery dynamic state-of-charge(SOC)and user charging decisions.Expressway network nodes were first extracted using the open Gaode Map API to establish a model that incorporates the expressway network and traffic flow fea-tures.A Gaussian mixture model is then employed to construct a SOC distribution model for mixed traffic flow.An innovative SOC dynamic translation model is then introduced to capture the dynamic characteristics of traffic flow SOC values.Based on this foun-dation,an EV charging decision model was developed which considers expressway node distinctions.EV travel characteristics are extracted from the NHTS2017 datasets to assist in constructing the model.Differentiated decision-making is achieved by utilizing improved Lognormal and Sigmoid functions.Finally,the proposed method is applied to a case study of the Lian-Huo expressway.An analysis of EV charging power converges with historical data and shows that the method accurately predicts the charging loads of EVs on expressways,thus revealing the efficacy of the proposed approach in predicting EV charging dynamics under expressway scenarios.
基金the National Key Research and Development Program of China(Grant No.2017YFB0702902)the National Natural Science Foundation of China(Grant Nos.51631008and 51771019)+1 种基金the National High Technology Research Program of China(Grant No.2012AA03A513)the 111 Project(No.B170003)
文摘Estimating long-term creep deformation and life of materials is an effective way to ensure the service safety and to reduce the cost of long-term integrity evaluation of high temperature structural materials.Since the 1980 s, the θ projection model has been widely used for predicting creep lives due to its ability to capture the characteristic transitions observed in creep curves obtained under constant true stress conditions. However, the creep rupture behavior under constant load or engineering stress conditions cannot be simulated accurately using this model because of the different stress states. In this paper, creep curves obtained under constant load conditions were analyzed using a modified θ projection model by considering the increase in true stress with creep deformation during the creep tests. This model is expressed as ε = θ_1(1-e^(-θ_2t)) + θ3 e^(θ_4e^θ5^εt)-1, and was validated using the creep curves of K465 and DZ125 superalloys tested at a range of temperatures and engineering stresses. Moreover, it was shown that the predictive capability of the modified θ projection model was significantly improved over the original one, as it reduces the prediction uncertainty from a range of 10% to 20% to below 5%. Meanwhile,it was shown that the model can be reasonably used for predicting constant stress creep conditions, when appropriate parameters are used. The prediction performance of the modified model will be discussed in another paper. The results of this study show great potential for the evaluation and assessment of the service safety of structural materials used in applications where designs are limited by creep deformation.
基金Aeronautical Science Foundation of China(98B52023),(04B52012)
文摘Semi-active landing gear can provide good performance of both landing impact and taxi situation,and has the ability for adapting to various ground conditions and operational conditions.A kind of Nonlinear Model Predictive Control algorithm(NMPC)for semi-active landing gears is developed in this paper.The NMPC algorithm uses Genetic Algorithm(GA)as the optimization technique and chooses damping performance of landing gear at touch down to be the optimization object.The valve's rate and magnitude limitations are also considered in the controller's design.A simulation model is built for the semi-active landing gear's damping process at touchdown.Drop tests are carried out on an experimental passive landing gear systerm to validate the parameters of the simulation model.The result of numerical simulation shows that the isolation of impact load at touchdown can be significantly improved compared to other control algorithms.The strongly nonlinear dynamics of semi-active landing gear coupled with control valve's rate and magnitude limitations are handled well with the proposed controller.
文摘The variation of fuel loads after a fire for three forest types, phododendron -Larix gmetinii forest, herb--Larix gmelinii forest and herb--Betula plalyphlla forest , in the northern forest area of Daxing’anling region was discussed. The dynamic models were developed by gray theory for estimating the fuels loads of arbor- shrub, herbs’ grass, litter, and semi-decomposed litter, inflamma ble fuel and the total fuels in each forest type. After a fire, the inflammabIe fuel loads in phododendron-- Larix gmelinii forest and in the herb- - Betula platyphlla fores was estimated at 10.958 t/hm2and 10.473 t/hm2 respectively’ by 13 years later. and that was 12.297 t/hm 2 in herb--Larix gmeliniiforest by 7 years later.. It was predicated that a big fire may occur after 10 years based on inflammable fuel biomass accumulated.
基金This study is supported by the National Natural Science Foundation of China(No.61801019).
文摘The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power grid.The three-phase unbalanced is closely related to power planning and load distribution.When the unbalance occurs,the safe operation of the electrical equipment will be seriously jeopardized.This paper proposes a Hierarchical Temporal Memory(HTM)-based three-phase unbalance prediction model consisted by the encoder for binary coding,the spatial pooler for frequency pattern learning,the temporal pooler for pattern sequence learning,and the sparse distributed representations classifier for unbalance prediction.Following the feasibility of spatial-temporal streaming data analysis,we adopted this brain-liked neural network to a real-time prediction for power load.We applied the model in five cities(Tangshan,Langfang,Qinhuangdao,Chengde,Zhangjiakou)of north China.We experimented with the proposed model and Long Short-term Memory(LSTM)model and analyzed the predict results and real currents.The results show that the predictions conform to the reality;compared to LSTM,the HTM-based prediction model shows enhanced accuracy and stability.The prediction model could serve for the overload warning and the load planning to provide high-quality power grid operation.
基金The MKE(the Ministry of Knowledge Economy),Korea,under the ITRC(Information Technology Research Center)support program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2011-C1090-1021-0010)Seoul Metropolitan Government,under the Seoul R & BD Program supervised by Seoul Business Agency(No.ST110039)
文摘We used an analytical high-level battery model to estimate the battery lifetime for a given load.The experimental results show that this model to predict battery lifetime under variable loads is more appropriate than that under constant loads.
文摘Recent industrial explosions globally have intensified the focus in mechanical engineering on designing infras-tructure systems and networks capable of withstanding blast loading.Initially centered on high-profile facilities such as embassies and petrochemical plants,this concern now extends to a wider array of infrastructures and facilities.Engineers and scholars increasingly prioritize structural safety against explosions,particularly to prevent disproportionate collapse and damage to nearby structures.Urbanization has further amplified the reliance on oil and gas pipelines,making them vital for urban life and prime targets for terrorist activities.Consequently,there is a growing imperative for computational engineering solutions to tackle blast loading on pipelines and mitigate associated risks to avert disasters.In this study,an empty pipe model was successfully validated under contact blast conditions using Abaqus software,a powerful tool in mechanical engineering for simulating blast effects on buried pipelines.Employing a Eulerian-Lagrangian computational fluid dynamics approach,the investigation extended to above-surface and below-surface blasts at standoff distances of 25 and 50 mm.Material descriptions in the numerical model relied on Abaqus’default mechanical models.Comparative analysis revealed varying pipe performance,with deformation decreasing as explosion-to-pipe distance increased.The explosion’s location relative to the pipe surface notably influenced deformation levels,a key finding highlighted in the study.Moreover,quantitative findings indicated varying ratios of plastic dissipation energy(PDE)for different blast scenarios compared to the contact blast(P0).Specifically,P1(25 mm subsurface blast)and P2(50 mm subsurface blast)showed approximately 24.07%and 14.77%of P0’s PDE,respectively,while P3(25 mm above-surface blast)and P4(50 mm above-surface blast)exhibited lower PDE values,accounting for about 18.08%and 9.67%of P0’s PDE,respectively.Utilising energy-absorbing materials such as thin coatings of ultra-high-strength concrete,metallic foams,carbon fiber-reinforced polymer wraps,and others on the pipeline to effectively mitigate blast damage is recommended.This research contributes to the advancement of mechanical engineering by providing insights and solutions crucial for enhancing the resilience and safety of underground pipelines in the face of blast events.
