Electrocatalytic reduction of CO_(2)into high energy-density fuels and value-added chemicals under mild conditions can promote the sustainable cycle of carbon and decrease current energy and environmental problems.Con...Electrocatalytic reduction of CO_(2)into high energy-density fuels and value-added chemicals under mild conditions can promote the sustainable cycle of carbon and decrease current energy and environmental problems.Constructing electrocatalyst with high activity,selectivity,stability,and low cost is really matter to realize industrial application of electrocatalytic CO_(2)reduction(ECR).Metal-nitrogen-carbon(M-N-C),especially Ni-N-C,display excellent performance,such as nearly 100%CO selectivity,high current density,outstanding tolerance,etc.,which is considered to possess broad application prospects.Based on the current research status,starting from the mechanism of ECR and the existence form of Ni active species,the latest research progress of Ni-N-C electrocatalysts in CO_(2)electroreduction is systematically summarized.An overview is emphatically interpreted on the regulatory strategies for activity optimization over Ni-N-C,including N coordination modulation,vacancy defects construction,morphology design,surface modification,heteroatom activation,and bimetallic cooperation.Finally,some urgent problems and future prospects on designing Ni-N-C catalysts for ECR are discussed.This review aims to provide the guidance for the design and development of Ni-N-C catalysts with practical application.展开更多
This paper presents a new non-linear formulation of the classical Vortex Lattice Method （VLM） approach for calculating the aerodynamic properties of lifting surfaces. The method accounts for the effects of viscosity...This paper presents a new non-linear formulation of the classical Vortex Lattice Method （VLM） approach for calculating the aerodynamic properties of lifting surfaces. The method accounts for the effects of viscosity, and due to its low computational cost, it represents a very good tool to perform rapid and accurate wing design and optimization procedures. The mathematical model is constructed by using two-dimensional viscous analyses of the wing span-wise sections, according to strip theory, and then coupling the strip viscous forces with the forces generated by the vortex rings distributed on the wing camber surface, calculated with a fully three-dimensional vortex lifting law. The numerical results obtained with the proposed method are validated with experimental data and show good agreement in predicting both the lift and pitching moment, as well as in predicting the wing drag. The method is applied to modifying the wing of an Unmanned Aerial System to increase its aerodynamic efficiency and to calculate the drag reductions obtained by an upper surface morphing technique for an adaptable regional aircraft wing.展开更多
Optimization algorithms are applied to resolve the second-order pileup(SOP)issue from high counting rates occurring in digital alpha spectroscopy.These are antlion optimizer(ALO)and particle swarm optimization(PSO)alg...Optimization algorithms are applied to resolve the second-order pileup(SOP)issue from high counting rates occurring in digital alpha spectroscopy.These are antlion optimizer(ALO)and particle swarm optimization(PSO)algorithms.Both optimization algorithms are coupled to one of the three proposed peak finder algorithms.Three custom time-domain algorithms are proposed for retrieving SOP peaks,namely peak seek,slope tangent,and fast array algorithms.In addition,an average combinational algorithm is applied.The time occurrence of the retrieved peaks is tested for an elimination of illusive pulses.Conventional methods are inaccurate and timeconsuming.ALO and PSO optimizations are used for the localization of retrieved peaks.Optimum cost values that achieve the best fitness values are demonstrated.Thus,the optimum positions of the detected peak heights are achieved.Evaluation metrics of the optimized algorithms and their influences on the retrieved peaks parameters are established.Comparisons among such algorithms are investigated,and the algorithms are inspected in terms of their computational time and average error.The peak seek algorithm achieves the lowest average computational error for pulse parameters(amplitude and position).However,the fast array algorithm introduces the largest average error for pulse parameters.In addition,the peak seek algorithm coupled with an ALO or PSO algorithm is observed to realize a better performance in terms of the optimum cost and computational time.By contrast,the performance of the peak seek recovery algorithm is improved using the PSO.Furthermore,the computational time of the peak optimization using the PSO is much better than that of the ALO algorithm.As a final conclusion,the accuracy of the peaks detected by the PSO surpasses that for the peaks detected by the ALO.The implemented peak retrieval algorithms are validated through a comparison with experimental results from previous studies.The proposed algorithms achieve a notable precision for compensation of the SOP peaks within the alpha ray spectroscopy at a high counting rate.展开更多
Muli-objective and multi disciplinary optimization mathodologies have progressed enormously in solving complex engineeing design problems.Turbomachines are essential energy conversion elements widely adopted in indust...Muli-objective and multi disciplinary optimization mathodologies have progressed enormously in solving complex engineeing design problems.Turbomachines are essential energy conversion elements widely adopted in industrial aplications,such as Muli objective and multi disciplinary optimization mathodologies have progressed enormously in solving complex engineeing design problems.展开更多
The main task of system reliability design is to find the best layout of components to maximize reliability or to minimize cost. A reliability optimization approach using neural networks to identify the choice of comp...The main task of system reliability design is to find the best layout of components to maximize reliability or to minimize cost. A reliability optimization approach using neural networks to identify the choice of components in series-parallel systems with multiple constraints is presented in this paper. The McCullochPittes neural network model is used in this approach. The design methods of the neural network construction and its energy function are described in detail. The optimal solutions of the reliability problem are obtained by minimizing the energy function of the neural networks. Simulation results show the reliability optimization approach using neural networks can find the optimal or near-optimal solutions for most of the problems in a relatively short time, it is a useful alternative for system reliability design of complex systems.展开更多
Objective To optimize experimental parameters for the photosensitization of 5-aminolevulinic acid (ALA) in promyelocytic leukemia cell HL60 and compare them with normal human peripheral blood mononuclear cell (PBMC). ...Objective To optimize experimental parameters for the photosensitization of 5-aminolevulinic acid (ALA) in promyelocytic leukemia cell HL60 and compare them with normal human peripheral blood mononuclear cell (PBMC). Methods ALA incubation time, wavelength applied to irradiate, concentration of ALA incubated, irradiation fluence may modulate the effect of 5-aminolevulinic acid based Photodynamic Therapy (ALA-PDT).The high-pressure mercury lamps of 400W served as light source, the interference filter of 410nm, 432nm, 545nm, 577nm were used to select the specific wavelength. Fluorescence microscope was used to detect the fluorescence intensity and location of protoporphyrin IX (PpIX) endogenously produced by ALA. MTT assay was used to measure the survival of cell. Flow cytometry with ANNEXIN V FITC kit (contains annexin V FITC, binding buffer and PI) was used to detect the mode of cell death. Results ① 1mmol/L ALA incubated 1×105/mL HL60 cell line for 4 hours, the maximum fluorescence of ALA induced PpIX was detected in cytomembrane. ② Irradiated with 410nm for 14.4J/cm2 can result in the minimum survivability of HL60 cell. ③ The main mode of HL60 cell death caused by ALA-PDT is necrosis. Conclusion ALA for 1mmol/L, 4 hours for dark incubation time, 410nm for irradiation wavelength, 14.4J/cm2 for irradiation fluence were the optimal parameters to selectively eliminate promyelocytic leukemia cell HL60 by ALA based PDT. The photosensitization of ALA based PDT caused the necrosis of HL60 cell, so it could be used for inactivation of certain leukemia cells.展开更多
Performance models provide insightful perspectives to predict performance and to propose optimization guidance.Although there has been much researches,pinpointing bottlenecks of various memory access patterns and reac...Performance models provide insightful perspectives to predict performance and to propose optimization guidance.Although there has been much researches,pinpointing bottlenecks of various memory access patterns and reaching high accurate prediction of both regular and irregular programs on various hardware configurations are still not trivial.This work proposes a novel model called process-RAM-feedback(PRF)to quantify the overhead of computation and data transmission time on general-purpose multi-core processors.The PRF model predicts the cost of instruction for singlecore by a directed acyclic graph(DAG)and the transmission time of memory access between each memory hierarchy through a newly designed cache simulator.By using performance modeling and feedback optimization method,this paper uses PRF model to analyze and optimize convolution,sparse matrix-vector multiplication and sn-sweep as case study for covering with typical regular kernel to irregular and data dependence.Through the PRF model,it obtains optimization guidance with various sparsity structures,algorithm designs,and instruction sets support on different data sizes.展开更多
Recently Guo Tao proposed a stochastic search algorithm in his PhD thesis for solving function optimization problems. He combined the subspace search method (a general multi-parent recombination strategy) with the pop...Recently Guo Tao proposed a stochastic search algorithm in his PhD thesis for solving function optimization problems. He combined the subspace search method (a general multi-parent recombination strategy) with the population hill-climbing method. The former keeps a global search for overall situation, and the latter keeps the convergence of the algorithm. Guo's algorithm has many advantages, such as the simplicity of its structure, the higher accuracy of its results, the wide range of its applications, and the robustness of its use. In this paper a preliminary theoretical analysis of the algorithm is given and some numerical experiments has been done by using Guo's algorithm for demonstrating the theoretical results. Three asynchronous parallel evolutionary algorithms with different granularities for MIMD machines are designed by parallelizing Guo's Algorithm.展开更多
Synthetic zeolite Na-A was prepared from Egyptian kaolinite by hydrothermal treatment to be used as an adsorbent for removal of phosphate from aqueous solutions. The present work deals with the application of response...Synthetic zeolite Na-A was prepared from Egyptian kaolinite by hydrothermal treatment to be used as an adsorbent for removal of phosphate from aqueous solutions. The present work deals with the application of response surface methodology (RSM) and central composite rotatable design (CCRD) for modeling and optimization of the effect of four operating variables on the removal of phosphate from aqueous solution using zeolite Na-A. The parameters were contact time (0.5 - 6 h), phosphate anion concentrations (10 - 30 mg/L), adsorbent dosage (0.05 - 0.1 g), and solution pH (2 - 7). A total of 26 tests were conducted using the synthetic zeolite Na-A according to the conditions predicted by the statistical design. In order to optimize removal of phosphate by synthetic zeolite Na-A, mathematical equations of quadratic polynomial model were derived from Design Expert Software (Version 6.0.5). Such equations are second-order response functions which represent the amount of phosphate adsorbed (mg/g) and the removal efficiency (%) and are expressed as functions of the selected operating parameters. Predicted values were found to be in good agreement and correlation with experimental results (R2 values of 0.918 and 0.905 for amount of phosphate adsorbed and removal efficiency of it, respectively). To understand the effect of the four variables for optimal removal of phosphate using zeolite Na-A, the models were presented as cube and 3-D response surface graphs. RSM and CCRD could efficiently be applied for the modeling of removing of phosphate from aqueous solution using zeolite Na-A and it is efficient way for obtaining information in a short time and with the fewer number of experiments.展开更多
This paper studies the solution technique to solve the DRAMA spares allocation optimization problem. DRAMA model is an analytic spare optimization model of a multi-item, multi-location, and two-echelon inventory syste...This paper studies the solution technique to solve the DRAMA spares allocation optimization problem. DRAMA model is an analytic spare optimization model of a multi-item, multi-location, and two-echelon inventory system. The computation of its system spares availability is much complicated. The objective function and constraint functions of DRAMA model could be written as the separable forms. A new bound heuristic algorithm has been presented by improving the bound heuristic algorithm for solving the reliability redundancy optimization problem (BHA in short). With the results, the proposed algorithm has been found to be more economical and effective than BHA to obtain the solutions of large DRAMA model. The new algorithm could be used to solve reliability redundancy optimization problems with the separable forms.展开更多
In the paper, a new mixed algorithm combined with schemes of nonmonotone line search, the systems of linear equations for higher order modification and sequential quadratic programming for constrained optimizations is...In the paper, a new mixed algorithm combined with schemes of nonmonotone line search, the systems of linear equations for higher order modification and sequential quadratic programming for constrained optimizations is presented. Under some weaker assumptions,without strict complementary condition, the algorithm is globally and superlinearly convergent.展开更多
Based on the non-equilibrium thermodynamics and energy and exergy analyses,a thermodynamic model of two-stage thermoelectric(TE)cooler(TTEC)driven by two-stage TE generator(TTEG)(TTEG-TTEC)combined TE device is establ...Based on the non-equilibrium thermodynamics and energy and exergy analyses,a thermodynamic model of two-stage thermoelectric(TE)cooler(TTEC)driven by two-stage TE generator(TTEG)(TTEG-TTEC)combined TE device is established with involving Thomson effect by fitting method of variable physical parameters of TE materials.Taking total number of TE elements as constraint,influences of number distributions of TE elements on three device performance indictors,that is,cooling load,maximum COP and maximum exergetic efficiency,are analyzed.Three number distributions of TE elements are optimized with three maximum performance indictors as the objectives,respectively.Influences of hot-junction temperature of TTEG and coldjunction temperature of TTEC on optimization results are analyzed,and difference between optimization results corresponding to three performance indicators are studied.Optimal performance intervals and optimal variable intervals are provided.Influences of Thomson effect on three general performance indicators,three optimal performance indicators and optimal variables are comparatively discussed.Thomson effect reduces three general performance indicators and three optimal performance indicators of device.When hot-and cold-junction temperatures of TTEG and TTEC are 450,305,325 and 295 K,respectively,Thomson effect reduced maximum cooling load,maximum COP and maximum exergetic efficiency from 9.528 W,9.043×10^(-2)and2.552%to 6.651 W,6.286×10^(-2)and 1.752%,respectively.展开更多
Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley a...Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.展开更多
Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in th...Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.展开更多
Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longe...Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.展开更多
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
With the increasing complexity of the current electromagnetic environment,excessive microwave radi-ation not only does harm to human health but also forms various electromagnetic interference to so-phisticated electro...With the increasing complexity of the current electromagnetic environment,excessive microwave radi-ation not only does harm to human health but also forms various electromagnetic interference to so-phisticated electronic instruments.Therefore,the design and preparation of electromagnetic absorbing composites represent an efficient approach to mitigate the current hazards of electromagnetic radiation.However,traditional electromagnetic absorbers are difficult to satisfy the demands of actual utilization in the face of new challenges,and emerging absorbents have garnered increasing attention due to their structure and performance-based advantages.In this review,several emerging composites of Mxene-based,biochar-based,chiral,and heat-resisting are discussed in detail,including their synthetic strategy,structural superiority and regulation method,and final optimization of electromagnetic absorption ca-pacity.These insights provide a comprehensive reference for the future development of new-generation electromagnetic-wave absorption composites.Moreover,the potential development directions of these emerging absorbers have been proposed as well.展开更多
Rock fragmentation is an important indicator for assessing the quality of blasting operations.However,accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters ...Rock fragmentation is an important indicator for assessing the quality of blasting operations.However,accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters and rock properties.For this reason,optimized by the Bayesian optimization algorithm(BOA),four hybrid machine learning models,including random forest,adaptive boosting,gradient boosting,and extremely randomized trees,were developed in this study.A total of 102 data sets with seven input parameters(spacing-to-burden ratio,hole depth-to-burden ratio,burden-to-hole diameter ratio,stemming length-to-burden ratio,powder factor,in situ block size,and elastic modulus)and one output parameter(rock fragment mean size,X_(50))were adopted to train and validate the predictive models.The root mean square error(RMSE),the mean absolute error(MAE),and the coefficient of determination(R^(2))were used as the evaluation metrics.The evaluation results demonstrated that the hybrid models showed superior performance than the standalone models.The hybrid model consisting of gradient boosting and BOA(GBoost-BOA)achieved the best prediction results compared with the other hybrid models,with the highest R^(2)value of 0.96 and the smallest values of RMSE and MAE of 0.03 and 0.02,respectively.Furthermore,sensitivity analysis was carried out to study the effects of input variables on rock fragmentation.In situ block size(XB),elastic modulus(E),and stemming length-to-burden ratio(T/B)were set as the main influencing factors.The proposed hybrid model provided a reliable prediction result and thus could be considered an alternative approach for rock fragment prediction in mining engineering.展开更多
In this article,our nonlinear theory and technology for reducing the uncertainties of high-impact ocean‒atmosphere event predictions,with the conditional nonlinear optimal perturbation(CNOP)method as its core,are revi...In this article,our nonlinear theory and technology for reducing the uncertainties of high-impact ocean‒atmosphere event predictions,with the conditional nonlinear optimal perturbation(CNOP)method as its core,are reviewed,and the“spring predictability barrier”problem for El Nino‒Southern Oscillation events and targeted observation issues for tropical cyclone forecasts are taken as two representative examples.Nonlinear theory reveals that initial errors of particular spatial structures,environmental conditions,and nonlinear processes contribute to significant prediction errors,whereas nonlinear technology provides a pioneering approach for reducing observational and forecast errors via targeted observations through the application of the CNOP method.Follow-up research further validates the scientific rigor of the theory in revealing the nonlinear mechanism of significant prediction errors,and relevant practical field campaigns for targeted observations verify the effectiveness of the technology in reducing prediction uncertainties.The CNOP method has achieved international recognition;furthermore,its applications further extend to ensemble forecasts for weather and climate and further enrich the nonlinear technology for reducing prediction uncertainties.It is expected that this nonlinear theory and technology will play a considerably important role in reducing prediction uncertainties for high-impact weather and climate events.展开更多
Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,th...Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.展开更多
基金financially supported by the National Natural Science Foundation of China(22278380,22108259)China Postdoctoral Science Foundation(2021M692911,2022T150589)
文摘Electrocatalytic reduction of CO_(2)into high energy-density fuels and value-added chemicals under mild conditions can promote the sustainable cycle of carbon and decrease current energy and environmental problems.Constructing electrocatalyst with high activity,selectivity,stability,and low cost is really matter to realize industrial application of electrocatalytic CO_(2)reduction(ECR).Metal-nitrogen-carbon(M-N-C),especially Ni-N-C,display excellent performance,such as nearly 100%CO selectivity,high current density,outstanding tolerance,etc.,which is considered to possess broad application prospects.Based on the current research status,starting from the mechanism of ECR and the existence form of Ni active species,the latest research progress of Ni-N-C electrocatalysts in CO_(2)electroreduction is systematically summarized.An overview is emphatically interpreted on the regulatory strategies for activity optimization over Ni-N-C,including N coordination modulation,vacancy defects construction,morphology design,surface modification,heteroatom activation,and bimetallic cooperation.Finally,some urgent problems and future prospects on designing Ni-N-C catalysts for ECR are discussed.This review aims to provide the guidance for the design and development of Ni-N-C catalysts with practical application.
基金the Natural Sciences and Engineering Research Council of Canada (NSERC) for the funding of the Canada Research Chair in Aircraft Modeling and Simulation Technologiesthe Canada Foundation of Innovation (CFI), the Ministerèdu Développement économique, de l’Innovation et de l’Exportation (MDEIE) and Hydra Technologies for the acquisition of the UAS-S4 using the Leaders Opportunity Funds+2 种基金the financial support obtained in the framework of the CRIAQ MDO-505 projectthe implication of our industrial partners Bombardier Aerospace and Thales CanadaNSERC for their support
文摘This paper presents a new non-linear formulation of the classical Vortex Lattice Method （VLM） approach for calculating the aerodynamic properties of lifting surfaces. The method accounts for the effects of viscosity, and due to its low computational cost, it represents a very good tool to perform rapid and accurate wing design and optimization procedures. The mathematical model is constructed by using two-dimensional viscous analyses of the wing span-wise sections, according to strip theory, and then coupling the strip viscous forces with the forces generated by the vortex rings distributed on the wing camber surface, calculated with a fully three-dimensional vortex lifting law. The numerical results obtained with the proposed method are validated with experimental data and show good agreement in predicting both the lift and pitching moment, as well as in predicting the wing drag. The method is applied to modifying the wing of an Unmanned Aerial System to increase its aerodynamic efficiency and to calculate the drag reductions obtained by an upper surface morphing technique for an adaptable regional aircraft wing.
文摘Optimization algorithms are applied to resolve the second-order pileup(SOP)issue from high counting rates occurring in digital alpha spectroscopy.These are antlion optimizer(ALO)and particle swarm optimization(PSO)algorithms.Both optimization algorithms are coupled to one of the three proposed peak finder algorithms.Three custom time-domain algorithms are proposed for retrieving SOP peaks,namely peak seek,slope tangent,and fast array algorithms.In addition,an average combinational algorithm is applied.The time occurrence of the retrieved peaks is tested for an elimination of illusive pulses.Conventional methods are inaccurate and timeconsuming.ALO and PSO optimizations are used for the localization of retrieved peaks.Optimum cost values that achieve the best fitness values are demonstrated.Thus,the optimum positions of the detected peak heights are achieved.Evaluation metrics of the optimized algorithms and their influences on the retrieved peaks parameters are established.Comparisons among such algorithms are investigated,and the algorithms are inspected in terms of their computational time and average error.The peak seek algorithm achieves the lowest average computational error for pulse parameters(amplitude and position).However,the fast array algorithm introduces the largest average error for pulse parameters.In addition,the peak seek algorithm coupled with an ALO or PSO algorithm is observed to realize a better performance in terms of the optimum cost and computational time.By contrast,the performance of the peak seek recovery algorithm is improved using the PSO.Furthermore,the computational time of the peak optimization using the PSO is much better than that of the ALO algorithm.As a final conclusion,the accuracy of the peaks detected by the PSO surpasses that for the peaks detected by the ALO.The implemented peak retrieval algorithms are validated through a comparison with experimental results from previous studies.The proposed algorithms achieve a notable precision for compensation of the SOP peaks within the alpha ray spectroscopy at a high counting rate.
文摘Muli-objective and multi disciplinary optimization mathodologies have progressed enormously in solving complex engineeing design problems.Turbomachines are essential energy conversion elements widely adopted in industrial aplications,such as Muli objective and multi disciplinary optimization mathodologies have progressed enormously in solving complex engineeing design problems.
基金Supported by the National Natural Science Foundation of China (60006002) and Natural Science Research Project of Education Depart-ment of Guangdong Province of China (02019)
文摘The main task of system reliability design is to find the best layout of components to maximize reliability or to minimize cost. A reliability optimization approach using neural networks to identify the choice of components in series-parallel systems with multiple constraints is presented in this paper. The McCullochPittes neural network model is used in this approach. The design methods of the neural network construction and its energy function are described in detail. The optimal solutions of the reliability problem are obtained by minimizing the energy function of the neural networks. Simulation results show the reliability optimization approach using neural networks can find the optimal or near-optimal solutions for most of the problems in a relatively short time, it is a useful alternative for system reliability design of complex systems.
文摘Objective To optimize experimental parameters for the photosensitization of 5-aminolevulinic acid (ALA) in promyelocytic leukemia cell HL60 and compare them with normal human peripheral blood mononuclear cell (PBMC). Methods ALA incubation time, wavelength applied to irradiate, concentration of ALA incubated, irradiation fluence may modulate the effect of 5-aminolevulinic acid based Photodynamic Therapy (ALA-PDT).The high-pressure mercury lamps of 400W served as light source, the interference filter of 410nm, 432nm, 545nm, 577nm were used to select the specific wavelength. Fluorescence microscope was used to detect the fluorescence intensity and location of protoporphyrin IX (PpIX) endogenously produced by ALA. MTT assay was used to measure the survival of cell. Flow cytometry with ANNEXIN V FITC kit (contains annexin V FITC, binding buffer and PI) was used to detect the mode of cell death. Results ① 1mmol/L ALA incubated 1×105/mL HL60 cell line for 4 hours, the maximum fluorescence of ALA induced PpIX was detected in cytomembrane. ② Irradiated with 410nm for 14.4J/cm2 can result in the minimum survivability of HL60 cell. ③ The main mode of HL60 cell death caused by ALA-PDT is necrosis. Conclusion ALA for 1mmol/L, 4 hours for dark incubation time, 410nm for irradiation wavelength, 14.4J/cm2 for irradiation fluence were the optimal parameters to selectively eliminate promyelocytic leukemia cell HL60 by ALA based PDT. The photosensitization of ALA based PDT caused the necrosis of HL60 cell, so it could be used for inactivation of certain leukemia cells.
基金Supported by the National Key Research and Development Program of China(No.2017YFB0202105,2016YFB0201305,2016YFB0200803,2016YFB0200300)the National Natural Science Foundation of China(No.61521092,91430218,31327901,61472395,61432018).
文摘Performance models provide insightful perspectives to predict performance and to propose optimization guidance.Although there has been much researches,pinpointing bottlenecks of various memory access patterns and reaching high accurate prediction of both regular and irregular programs on various hardware configurations are still not trivial.This work proposes a novel model called process-RAM-feedback(PRF)to quantify the overhead of computation and data transmission time on general-purpose multi-core processors.The PRF model predicts the cost of instruction for singlecore by a directed acyclic graph(DAG)and the transmission time of memory access between each memory hierarchy through a newly designed cache simulator.By using performance modeling and feedback optimization method,this paper uses PRF model to analyze and optimize convolution,sparse matrix-vector multiplication and sn-sweep as case study for covering with typical regular kernel to irregular and data dependence.Through the PRF model,it obtains optimization guidance with various sparsity structures,algorithm designs,and instruction sets support on different data sizes.
基金Supported by the Natonal Natural Science Foundation of China (No. 70071042 60073043)the National 863 Hi-Tech Project of Chi
文摘Recently Guo Tao proposed a stochastic search algorithm in his PhD thesis for solving function optimization problems. He combined the subspace search method (a general multi-parent recombination strategy) with the population hill-climbing method. The former keeps a global search for overall situation, and the latter keeps the convergence of the algorithm. Guo's algorithm has many advantages, such as the simplicity of its structure, the higher accuracy of its results, the wide range of its applications, and the robustness of its use. In this paper a preliminary theoretical analysis of the algorithm is given and some numerical experiments has been done by using Guo's algorithm for demonstrating the theoretical results. Three asynchronous parallel evolutionary algorithms with different granularities for MIMD machines are designed by parallelizing Guo's Algorithm.
文摘Synthetic zeolite Na-A was prepared from Egyptian kaolinite by hydrothermal treatment to be used as an adsorbent for removal of phosphate from aqueous solutions. The present work deals with the application of response surface methodology (RSM) and central composite rotatable design (CCRD) for modeling and optimization of the effect of four operating variables on the removal of phosphate from aqueous solution using zeolite Na-A. The parameters were contact time (0.5 - 6 h), phosphate anion concentrations (10 - 30 mg/L), adsorbent dosage (0.05 - 0.1 g), and solution pH (2 - 7). A total of 26 tests were conducted using the synthetic zeolite Na-A according to the conditions predicted by the statistical design. In order to optimize removal of phosphate by synthetic zeolite Na-A, mathematical equations of quadratic polynomial model were derived from Design Expert Software (Version 6.0.5). Such equations are second-order response functions which represent the amount of phosphate adsorbed (mg/g) and the removal efficiency (%) and are expressed as functions of the selected operating parameters. Predicted values were found to be in good agreement and correlation with experimental results (R2 values of 0.918 and 0.905 for amount of phosphate adsorbed and removal efficiency of it, respectively). To understand the effect of the four variables for optimal removal of phosphate using zeolite Na-A, the models were presented as cube and 3-D response surface graphs. RSM and CCRD could efficiently be applied for the modeling of removing of phosphate from aqueous solution using zeolite Na-A and it is efficient way for obtaining information in a short time and with the fewer number of experiments.
文摘This paper studies the solution technique to solve the DRAMA spares allocation optimization problem. DRAMA model is an analytic spare optimization model of a multi-item, multi-location, and two-echelon inventory system. The computation of its system spares availability is much complicated. The objective function and constraint functions of DRAMA model could be written as the separable forms. A new bound heuristic algorithm has been presented by improving the bound heuristic algorithm for solving the reliability redundancy optimization problem (BHA in short). With the results, the proposed algorithm has been found to be more economical and effective than BHA to obtain the solutions of large DRAMA model. The new algorithm could be used to solve reliability redundancy optimization problems with the separable forms.
文摘In the paper, a new mixed algorithm combined with schemes of nonmonotone line search, the systems of linear equations for higher order modification and sequential quadratic programming for constrained optimizations is presented. Under some weaker assumptions,without strict complementary condition, the algorithm is globally and superlinearly convergent.
基金supported by the National Natural Science Foundation of China(Grant No.52171317)。
文摘Based on the non-equilibrium thermodynamics and energy and exergy analyses,a thermodynamic model of two-stage thermoelectric(TE)cooler(TTEC)driven by two-stage TE generator(TTEG)(TTEG-TTEC)combined TE device is established with involving Thomson effect by fitting method of variable physical parameters of TE materials.Taking total number of TE elements as constraint,influences of number distributions of TE elements on three device performance indictors,that is,cooling load,maximum COP and maximum exergetic efficiency,are analyzed.Three number distributions of TE elements are optimized with three maximum performance indictors as the objectives,respectively.Influences of hot-junction temperature of TTEG and coldjunction temperature of TTEC on optimization results are analyzed,and difference between optimization results corresponding to three performance indicators are studied.Optimal performance intervals and optimal variable intervals are provided.Influences of Thomson effect on three general performance indicators,three optimal performance indicators and optimal variables are comparatively discussed.Thomson effect reduces three general performance indicators and three optimal performance indicators of device.When hot-and cold-junction temperatures of TTEG and TTEC are 450,305,325 and 295 K,respectively,Thomson effect reduced maximum cooling load,maximum COP and maximum exergetic efficiency from 9.528 W,9.043×10^(-2)and2.552%to 6.651 W,6.286×10^(-2)and 1.752%,respectively.
基金supported by the General Program of the National Natural Science Foundation of China(No.52274326)the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202109)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553).
文摘Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.
基金supported by the National Natural Science Foundation of China [grant numbers 42088101 and 42375048]。
文摘Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.
基金supported by a Horizontal Project on the Development of a Hybrid Energy Storage Simulation Model for Wind Power Based on an RT-LAB Simulation System(PH2023000190)the Inner Mongolia Natural Science Foundation Project and the Optimization of Exergy Efficiency of a Hybrid Energy Storage System with Crossover Control for Wind Power(2023JQ04).
文摘Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
基金supported by the Surface Project of Local De-velopment in Science and Technology Guided by Central Govern-ment(No.2021ZYD0041)the National Natural Science Founda-tion of China(Nos.52377026 and 52301192)+3 种基金the Natural Science Foundation of Shandong Province(No.ZR2019YQ24)the Taishan Scholars and Young Experts Program of Shandong Province(No.tsqn202103057)the Special Financial of Shandong Province(Struc-tural Design of High-efficiency Electromagnetic Wave-absorbing Composite Materials and Construction of Shandong Provincial Tal-ent Teams)the“Sanqin Scholars”Innovation Teams Project of Shaanxi Province(Clean Energy Materials and High-Performance Devices Innovation Team of Shaanxi Dongling Smelting Co.,Ltd.).
文摘With the increasing complexity of the current electromagnetic environment,excessive microwave radi-ation not only does harm to human health but also forms various electromagnetic interference to so-phisticated electronic instruments.Therefore,the design and preparation of electromagnetic absorbing composites represent an efficient approach to mitigate the current hazards of electromagnetic radiation.However,traditional electromagnetic absorbers are difficult to satisfy the demands of actual utilization in the face of new challenges,and emerging absorbents have garnered increasing attention due to their structure and performance-based advantages.In this review,several emerging composites of Mxene-based,biochar-based,chiral,and heat-resisting are discussed in detail,including their synthetic strategy,structural superiority and regulation method,and final optimization of electromagnetic absorption ca-pacity.These insights provide a comprehensive reference for the future development of new-generation electromagnetic-wave absorption composites.Moreover,the potential development directions of these emerging absorbers have been proposed as well.
基金National Natural Science Foundation of China,Grant/Award Number:52374153。
文摘Rock fragmentation is an important indicator for assessing the quality of blasting operations.However,accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters and rock properties.For this reason,optimized by the Bayesian optimization algorithm(BOA),four hybrid machine learning models,including random forest,adaptive boosting,gradient boosting,and extremely randomized trees,were developed in this study.A total of 102 data sets with seven input parameters(spacing-to-burden ratio,hole depth-to-burden ratio,burden-to-hole diameter ratio,stemming length-to-burden ratio,powder factor,in situ block size,and elastic modulus)and one output parameter(rock fragment mean size,X_(50))were adopted to train and validate the predictive models.The root mean square error(RMSE),the mean absolute error(MAE),and the coefficient of determination(R^(2))were used as the evaluation metrics.The evaluation results demonstrated that the hybrid models showed superior performance than the standalone models.The hybrid model consisting of gradient boosting and BOA(GBoost-BOA)achieved the best prediction results compared with the other hybrid models,with the highest R^(2)value of 0.96 and the smallest values of RMSE and MAE of 0.03 and 0.02,respectively.Furthermore,sensitivity analysis was carried out to study the effects of input variables on rock fragmentation.In situ block size(XB),elastic modulus(E),and stemming length-to-burden ratio(T/B)were set as the main influencing factors.The proposed hybrid model provided a reliable prediction result and thus could be considered an alternative approach for rock fragment prediction in mining engineering.
基金sponsored by the National Natural Science Foun-dation of China(Grant No.42330111).
文摘In this article,our nonlinear theory and technology for reducing the uncertainties of high-impact ocean‒atmosphere event predictions,with the conditional nonlinear optimal perturbation(CNOP)method as its core,are reviewed,and the“spring predictability barrier”problem for El Nino‒Southern Oscillation events and targeted observation issues for tropical cyclone forecasts are taken as two representative examples.Nonlinear theory reveals that initial errors of particular spatial structures,environmental conditions,and nonlinear processes contribute to significant prediction errors,whereas nonlinear technology provides a pioneering approach for reducing observational and forecast errors via targeted observations through the application of the CNOP method.Follow-up research further validates the scientific rigor of the theory in revealing the nonlinear mechanism of significant prediction errors,and relevant practical field campaigns for targeted observations verify the effectiveness of the technology in reducing prediction uncertainties.The CNOP method has achieved international recognition;furthermore,its applications further extend to ensemble forecasts for weather and climate and further enrich the nonlinear technology for reducing prediction uncertainties.It is expected that this nonlinear theory and technology will play a considerably important role in reducing prediction uncertainties for high-impact weather and climate events.
基金supported by Yunnan Provincial Basic Research Project(202401AT070344,202301AT070443)National Natural Science Foundation of China(62263014,52207105)+1 种基金Yunnan Lancang-Mekong International Electric Power Technology Joint Laboratory(202203AP140001)Major Science and Technology Projects in Yunnan Province(202402AG050006).
文摘Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.
