System reliability optimization problems have been widely discussed to maximize system reliability with resource constraints.Bimbaum importance is a wellknown method for evaluating the effect of component reliability ...System reliability optimization problems have been widely discussed to maximize system reliability with resource constraints.Bimbaum importance is a wellknown method for evaluating the effect of component reliability on system reliability.Many importance measures(IMs)are extended for binary,multistate,and continuous systems from different aspects based on the Bimbaum importance.Recently,these IMs have been applied in allocating limited resources to the component to maximize system performance.Therefore,the significance of Bimbaum importance is illustrated from the perspective of probability principle and gradient geometrical sense.Furthermore,the equations of various extended IMs are provided subsequently.The rules for simple optimization problems are summarized to enhance system reliability by using ranking or heuristic methods based on IMs.The importance-based optimization algorithms for complex or large-scale systems are generalized to obtain remarkable solutions by using IM-based local search or simplification methods.Furthermore,a general framework driven by IM is developed to solve optimization problems.Finally,some challenges in system reliability optimization that need to be solved in the future are presented.展开更多
Structural design optimization has always been a topic of concern in industry because good design can improve the safety and economic efficiency of structures during their service periods.Selecting the appropriate opt...Structural design optimization has always been a topic of concern in industry because good design can improve the safety and economic efficiency of structures during their service periods.Selecting the appropriate optimization algorithm is the key to solving structural optimal design problems.In this study,a new global optimization idea is proposed and named the moving baseline strategy.A baseline is initially set and will be repeatedly moving upward or downward to approach the optimal value.The proposed strategy is a simple but effective,general,and stable algorithm that can be used to solve constrained and unconstrained structural optimization problems.Different from traditional gradient-based,stochastic and heuristic algorithms,the developed algorithm provides a completely new idea to solve global or local optimization problems.Some unconstrained and constrained numerical benchmark examples are used to test the proposed methodology.In addition,structural optimal design problems of a ten-bar planar truss structure and a hypersonic wing structure(X-37B)are utilized to verify the effectiveness of the developed strategy in addressing structural design optimization problems in engineering.展开更多
Cancer-beating molecules (CBMs) are abundant in many types of food and potentially anti-cancer therapeutic agents. In the previous work, researchers introduced a network-based machine learning platform to identify the...Cancer-beating molecules (CBMs) are abundant in many types of food and potentially anti-cancer therapeutic agents. In the previous work, researchers introduced a network-based machine learning platform to identify the cancer-beating molecules, for example,?comparing the similarities in the molecular network between approved anticancer drug and food molecules. Herein, we aim to build on this work to enhance the accuracy of predicting food molecules. In this project, we improve supervised learning approaches by applying Soft Voting algorithm to seven machine learning algorithms: Support Vector Machine with Radial Basis Function (SVM with RBF kernel), multilayer perceptron neural network?(MLP), Random forest, Decision trees,?Gaussian Naive Bayes, Adaboosting, and Bagging. As a result, the accuracy in the dataset of 50 food molecules utilized increased from 82% to 87%, achieving a significant improvement in the precision of?predicting anti-cancer molecules.展开更多
Based on the analysis of characteristics and advantages of HSO(harmony search optimization) algorithm, HSO was used in reservoir engineering assisted history matching of Kareem reservoir in Amal field in the Gulf of S...Based on the analysis of characteristics and advantages of HSO(harmony search optimization) algorithm, HSO was used in reservoir engineering assisted history matching of Kareem reservoir in Amal field in the Gulf of Suez, Egypt. HSO algorithm has the following advantages:(1) The good balance between exploration and exploitation techniques during searching for optimal solutions makes the HSO algorithm robust and efficient.(2) The diversity of generated solutions is more effectively controlled by two components, making it suitable for highly non-linear problems in reservoir engineering history matching.(3) The integration between the three components(harmony memory values, pitch adjusting and randomization) of the HSO helps in finding unbiased solutions.(4) The implementation process of the HSO algorithm is much easier. The HSO algorithm and two other commonly used algorithms(genetic and particle swarm optimization algorithms) were used in three reservoir engineering history match questions of different complex degrees, which are two material balance history matches of different scales and one reservoir history matching. The results were compared, which proves the superiority and validity of HSO. The results of Kareem reservoir history matching show that using the HSO algorithm as the optimization method in the assisted history matching workflow improves the simulation quality and saves solution time significantly.展开更多
The growing demands for high speed connectivity to keep pace with bandwidth intensive applications and services have spawned the idea of developing PONs with capabilities beyond those of copper and wireless-based tech...The growing demands for high speed connectivity to keep pace with bandwidth intensive applications and services have spawned the idea of developing PONs with capabilities beyond those of copper and wireless-based technologies in access network. In this article, an approach for the design of an energy efficient bandwidth allocation mechanism for the shared upstream communication link in the Fiber to the Home (FTTH) access network is presented and evaluated using Mixed Integer Linear Programming (MILP) model. In the MILP model, two objective functions for minimization of power consumption and minimization of blocking were evaluated. The results have shown that with the objective of power minimization approach, Optical Network Terminals (ONTs) are efficiently grouped to the minimum number of active networking Optical Line Terminal (OLT) switches, traffic is groomed, ports are efficiently utilized, and hence total power consumption is minimized. Results have shown that with energy efficient bandwidth allocation approach consideration, energy savings can reach up to 80% for different examined traffic loads following uniform distribution.展开更多
It is demonstrated that the recently introduced semantic intelligence spontaneously maintains bounded logical and quantal error on each and every semantic trajectory, unlike its algorithmic counterpart which is not ab...It is demonstrated that the recently introduced semantic intelligence spontaneously maintains bounded logical and quantal error on each and every semantic trajectory, unlike its algorithmic counterpart which is not able to. This result verifies the conclusion about the assignment of equal evolutionary value to the motion on the set of all the semantic trajectories sharing the same homeostatic pattern. The evolutionary value of permanent and spontaneous maintenance of boundedness of logical and quantal error on each and every semantic trajectory is to make available spontaneous maintenance of the notion of a kind intact in the long run.展开更多
Aimed at improving the insufficient search ability of constraint differential evolution with single constraint handling technique when solving complex optimization problem, this paper proposes a constraint differentia...Aimed at improving the insufficient search ability of constraint differential evolution with single constraint handling technique when solving complex optimization problem, this paper proposes a constraint differential evolution algorithm?based on ensemble of constraint handling techniques and multi-population?framework, called ECMPDE. First, handling three improved variants of differential evolution algorithms are dynamically matched with two constraint handling techniques through the constraint allocation mechanism. Each combination includes three variants with corresponding constraint handling technique?and these combinations are in the set. Second, the population is divided into three smaller subpopulations and one larger reward subpopulation. Then a combination with three constraint algorithms is randomly selected from the set, and the three constraint algorithms are run in three sub-populations respectively. According to the improvement of fitness value, the optimal constraint?algorithm is selected to run on the reward sub-population, which can share?information and close cooperation among populations. In order to verify the effectiveness of the proposed algorithm, 12 standard constraint optimization problems?and 10 engineering constraint optimization problems are tested. The experimental results show that ECMPDE is an effective algorithm for solving constraint optimization problems.展开更多
This study presents an optimization technique and design for a stand-alone photovoltaic (PV) system to provide the required electricity for a single residential household in remote areas. From the basic solar componen...This study presents an optimization technique and design for a stand-alone photovoltaic (PV) system to provide the required electricity for a single residential household in remote areas. From the basic solar components analysis, the irradiance on tilted surface is derived and compared to that on horizontal surface for Furu-Awa locality to infer the appropriate tilt angle (β) that maximizes the collection of solar energy. Seven optimum values of β applicable to the PV network were then derived depending of the period of the year and this simulation resulted that the panels are to be adjusted seven times a year. The optimization technique for load demand based on total apparent power of the household appliances produces an increase of 18% compared to the simple case of the PV components design using active power but leads to the optimum configuration that meets the real load demand of the household. Following the sizing of the station, reliability tests simulations were conducted for a one year corresponding period to infer the sensitivity of power supply to initial state of charge, to check the system autonomy and to evaluate the effect of random variation of the load on the smooth functioning of the PV system using a pseudo random number generator. This analysis shows that the minimum capacity of the battery for normal run of the Plan is 22.2% and that with random fluctuation of load, there will be periods of the year where the system experiences power failure depending on how important is the variation. The result of the study may imply a small increase in the cost of the entire plant but improves the stability and flexibility of such a station.展开更多
This review paper summarizes constructal design progress performed by the authors for eight types of heat sinks with ten performance indexes being taken as the optimization objectives,respectively,by combining the met...This review paper summarizes constructal design progress performed by the authors for eight types of heat sinks with ten performance indexes being taken as the optimization objectives,respectively,by combining the methods of theoretical analysis and numerical calculation.The eight types of heat sinks are uniform height rectangular fin heat sink,non-uniform height rectangular fin heat sink,inline cylindrical pin-fin heat sink(ICPHS),plate single-row pin fin heat sink(PSRPHS),plate inline pin fin heat sink(PIPHS),plate staggered pin fin heat sink(PSPHS),single-layered microchannel heat sink(SLMCHS)with rectangular cross sections and double-layered microchannel heat sink(DLMCHS)with rectangular cross sections,respectively.And the ten performance indexes are heat transfer rate maximization,maximum thermal resistance minimization,minimization of equivalent thermal resistance which is defined based on the entransy dissipation rate(equivalent thermal resistance for short),field synergy number maximization,entropy generation rate minimization,operation cost minimization,thermo-economic function value minimization,pressure drop minimization,enhanced heat transfer factor maximization and efficiency evaluation criterion number maximization,respectively.The optimal constructs of the eight types of heat sinks with different constraints and based on the different optimization objectives are compared with each other.The results indicated that the optimal constructs mostly are different based on different optimization objectives under the same boundary condition.The optimization objective should be suitable chosen based on the focus when the constructal design for one heat sink is performed.The results obtained herein have some important theoretical significances and application values,and can provide scientific bases and theoretical guidelines for the thermal design of real heat sinks and their applications.展开更多
The existing multi-objective wheel profile optimization methods mainly consist of three sub-modules:(1)wheel profile generation,(2)multi-body dynamics simulation,and(3)an optimization algorithm.For the first module,a ...The existing multi-objective wheel profile optimization methods mainly consist of three sub-modules:(1)wheel profile generation,(2)multi-body dynamics simulation,and(3)an optimization algorithm.For the first module,a comparably conservative rotary-scaling finetuning(RSFT)method,which introduces two design variables and an empirical formula,is proposed to fine-tune the traditional wheel profiles for improving their engineering applicability.For the second module,for the TRAXX locomotives serving on the Blankenburg–Rubeland line,an optimization function representing the relationship between the wheel profile and the wheel–rail wear number is established based on Kriging surrogate model(KSM).For the third module,a method combining the regression capability of KSM with the iterative computing power of particle swarm optimization(PSO)is proposed to quickly and reliably implement the task of optimizing wheel profiles.Finally,with the RSFT–KSM–PSO method,we propose two wear-resistant wheel profiles for the TRAXX locomotives serving on the Blankenburg–Rubeland line,namely S1002-S and S1002-M.The S1002-S profile minimizes the total wear number by 30%,while the S1002-M profile makes the wear distribution more uniform through a proper sacrifice of the tread wear number,and the total wear number is reduced by 21%.The quasi-static and hunting stability tests further demonstrate that the profile designed by the RSFT–KSM–PSO method is promising for practical engineering applications.展开更多
This paper presents new hybrid methods for the identification of optimal topologies by combining the teaching-learning based optimization(TLBO)and the method of moving asymptotes(MMA).The topology optimization problem...This paper presents new hybrid methods for the identification of optimal topologies by combining the teaching-learning based optimization(TLBO)and the method of moving asymptotes(MMA).The topology optimization problem is parameterizing with a low dimensional explicit method called moving morphable components(MMC),to make the use of evolutionary algorithms more efficient.Gradient-based solvers have good performance in solving large-scale topology optimization problems.However,in unconventional cases same as crashworthiness design in which there is numerical noise in the gradient information,the uses of these algorithms are unsuitable.The standard evolutionary algorithms can solve such problems since they don’t need gradient information.However,they have a high computational cost.This paper is based upon the idea of combining metaheuristics with mathematical programming to handle the probable noises and have faster convergence speed.Due to the ease of computations,the compliance minimization problem is considered as the case study and the artificial noise is added in gradient information.展开更多
The laser scanning system based on Simultaneous Localization and Mapping(SLAM)technology has the advantages of low cost,high precision and high efficiency.It has drawn wide attention in the field of surveying and mapp...The laser scanning system based on Simultaneous Localization and Mapping(SLAM)technology has the advantages of low cost,high precision and high efficiency.It has drawn wide attention in the field of surveying and mapping in recent years.Although real-time data acquisition can be achieved using SLAM technology,the precision of the data can’t be ensured,and inconsistency exists in the acquired point cloud.In order to improve the precision of the point cloud obtained by this kind of system,this paper presents a hierarchical point cloud global optimization algorithm.Firstly,the“point-to-plane”iterative closest point(ICP)algorithm is used to match the overlapping point clouds to form constraints between the trajectories of the scanning system.Then a pose graph is constructed to optimize the trajectory.Finally,the optimized trajectory is used to refine the point cloud.The computational efficiency is improved by decomposing the optimization process into two levels,i.e.local level and global level.The experimental results show that the RMSE of the distance between the corresponding points in overlapping areas is reduced by about 50%after optimization,and the internal inconsistency is effectively eliminated.展开更多
A comprehensive mathematical model is developed to simulate the interactions of the complex processes that take place in typical catalytic chemical reactors. This mathematical model includes correlations representing ...A comprehensive mathematical model is developed to simulate the interactions of the complex processes that take place in typical catalytic chemical reactors. This mathematical model includes correlations representing various modes of mass transport and chemical reactions. To illustrate the application and value of this approach for reactor optimizations, the model is applied to the case of series reactions with a desirable intermediate compound and the risk of degradation of this compound if the process conditions are not optimized. The modeling results show that in such cases, which are very common in practice, replacing the conventional uniform catalyst distribution with a novel non-uniform distribution will significantly improve the performance of the reactor and the production of the desirable compound. Various catalyst distribution options are compared, and a novel non-uniform loading of catalyst is identified that gives a much better performance compared to the conventional approach. The model is versatile and useful for both the design as well as the optimization of the catalytic fixed-bed reactors in a wide variety of reactor and reaction conditions.展开更多
The 2MW wind turbine tower is considered as the baseline configuration for structural optimization.The design variables consist of the thickness and height located at the top tower junction.The relationships between t...The 2MW wind turbine tower is considered as the baseline configuration for structural optimization.The design variables consist of the thickness and height located at the top tower junction.The relationships between the design variables and the optimization objectives(mass,equivalent stress,top displacement and fatigue life)are mapped on the basis of uniform design and regression analysis.Subsequently,five solutions are developed by an algorithm,NSGA-III.According to their efficiency and applicability,the most suitable solution is found.This approach yields a decrease of 0.48%in the mass,a decrease of 54.48%in the equivalent stress and an increase of 8.14%in fatigue life,as compared with existing tower designs.An improved wind turbine tower is obtained for this practice.展开更多
With the widespread application of distributed systems, many problems need to be solved urgently. How to design distributed optimization strategies has become a research hotspot. This article focuses on the solution r...With the widespread application of distributed systems, many problems need to be solved urgently. How to design distributed optimization strategies has become a research hotspot. This article focuses on the solution rate of the distributed convex optimization algorithm. Each agent in the network has its own convex cost function. We consider a gradient-based distributed method and use a push-pull gradient algorithm to minimize the total cost function. Inspired by the current multi-agent consensus cooperation protocol for distributed convex optimization algorithm, a distributed convex optimization algorithm with finite time convergence is proposed and studied. In the end, based on a fixed undirected distributed network topology, a fast convergent distributed cooperative learning method based on a linear parameterized neural network is proposed, which is different from the existing distributed convex optimization algorithms that can achieve exponential convergence. The algorithm can achieve finite-time convergence. The convergence of the algorithm can be guaranteed by the Lyapunov method. The corresponding simulation examples also show the effectiveness of the algorithm intuitively. Compared with other algorithms, this algorithm is competitive.展开更多
This work concerns an experimental and numerical study of energy losses in a typical oven usually used in the agro-food craft sector in Burkina Faso. The experimental results were obtained by infrared thermography of ...This work concerns an experimental and numerical study of energy losses in a typical oven usually used in the agro-food craft sector in Burkina Faso. The experimental results were obtained by infrared thermography of the oven and by monitoring the evolution of the wall temperatures using thermocouples connected to a data acquisition system. These results indicate that the energy losses are mainly through the walls of the oven. The numerical study based on the energy balance and corroborated by the experimental study made it possible to quantify these losses of energy which represents almost half of the fuel used. These results will allow us to work on a new, more efficient oven model for the grilling sector in Burkina Faso.展开更多
Efficient tuning of the coefficients used by proportional-integral-derivative(PID)controllers enhances their performance.For highly non-linear systems,optimization algorithms are required to make the PID controllers m...Efficient tuning of the coefficients used by proportional-integral-derivative(PID)controllers enhances their performance.For highly non-linear systems,optimization algorithms are required to make the PID controllers more responsive to disturbances.The production of tert-amyl-methyl-ether(TAME),an essential additive for gasoline,in reactive distillation columns integrates highly non-linear reaction and separation processes.On the other hand,TAME distillation is an azeotrope distillation process,therefore non-linearity of this process is more complex than that of conventional distillation.PID-controller tuning methods applying a genetic algorithm(GA)and a particle swarm optimization(PSO)algorithm are compared using a dynamic simulation that integrates the optimization algorithms with the HYSYS process simulator.The PID controller response trends are analyzed following the introduction of a significant disturbance to the TAME reactive distillation column(i.e.,a ten percent change in the methanol feed temperature).The PSO PID controller tuning method that minimizes the integral of the absolute error(IAE)as its objective function significantly outperforms the GA tuning method.The novel PID-tuning methodology developed has more extensive application potential.展开更多
基金This work was funded by the National Natural Science Foundation of China(GrantNos.71771186,71631001,and 71871181)and the 111 Project(GrantNo.B13044).
文摘System reliability optimization problems have been widely discussed to maximize system reliability with resource constraints.Bimbaum importance is a wellknown method for evaluating the effect of component reliability on system reliability.Many importance measures(IMs)are extended for binary,multistate,and continuous systems from different aspects based on the Bimbaum importance.Recently,these IMs have been applied in allocating limited resources to the component to maximize system performance.Therefore,the significance of Bimbaum importance is illustrated from the perspective of probability principle and gradient geometrical sense.Furthermore,the equations of various extended IMs are provided subsequently.The rules for simple optimization problems are summarized to enhance system reliability by using ranking or heuristic methods based on IMs.The importance-based optimization algorithms for complex or large-scale systems are generalized to obtain remarkable solutions by using IM-based local search or simplification methods.Furthermore,a general framework driven by IM is developed to solve optimization problems.Finally,some challenges in system reliability optimization that need to be solved in the future are presented.
基金the National Nature Science Foundation of China(Nos.11872089,11572024,11432002)the Defense Industrial Technology Development Programs(Nos.JCK Y2016204B101,JCKY2017601B001,JCKY2018601B001)。
文摘Structural design optimization has always been a topic of concern in industry because good design can improve the safety and economic efficiency of structures during their service periods.Selecting the appropriate optimization algorithm is the key to solving structural optimal design problems.In this study,a new global optimization idea is proposed and named the moving baseline strategy.A baseline is initially set and will be repeatedly moving upward or downward to approach the optimal value.The proposed strategy is a simple but effective,general,and stable algorithm that can be used to solve constrained and unconstrained structural optimization problems.Different from traditional gradient-based,stochastic and heuristic algorithms,the developed algorithm provides a completely new idea to solve global or local optimization problems.Some unconstrained and constrained numerical benchmark examples are used to test the proposed methodology.In addition,structural optimal design problems of a ten-bar planar truss structure and a hypersonic wing structure(X-37B)are utilized to verify the effectiveness of the developed strategy in addressing structural design optimization problems in engineering.
文摘Cancer-beating molecules (CBMs) are abundant in many types of food and potentially anti-cancer therapeutic agents. In the previous work, researchers introduced a network-based machine learning platform to identify the cancer-beating molecules, for example,?comparing the similarities in the molecular network between approved anticancer drug and food molecules. Herein, we aim to build on this work to enhance the accuracy of predicting food molecules. In this project, we improve supervised learning approaches by applying Soft Voting algorithm to seven machine learning algorithms: Support Vector Machine with Radial Basis Function (SVM with RBF kernel), multilayer perceptron neural network?(MLP), Random forest, Decision trees,?Gaussian Naive Bayes, Adaboosting, and Bagging. As a result, the accuracy in the dataset of 50 food molecules utilized increased from 82% to 87%, achieving a significant improvement in the precision of?predicting anti-cancer molecules.
文摘Based on the analysis of characteristics and advantages of HSO(harmony search optimization) algorithm, HSO was used in reservoir engineering assisted history matching of Kareem reservoir in Amal field in the Gulf of Suez, Egypt. HSO algorithm has the following advantages:(1) The good balance between exploration and exploitation techniques during searching for optimal solutions makes the HSO algorithm robust and efficient.(2) The diversity of generated solutions is more effectively controlled by two components, making it suitable for highly non-linear problems in reservoir engineering history matching.(3) The integration between the three components(harmony memory values, pitch adjusting and randomization) of the HSO helps in finding unbiased solutions.(4) The implementation process of the HSO algorithm is much easier. The HSO algorithm and two other commonly used algorithms(genetic and particle swarm optimization algorithms) were used in three reservoir engineering history match questions of different complex degrees, which are two material balance history matches of different scales and one reservoir history matching. The results were compared, which proves the superiority and validity of HSO. The results of Kareem reservoir history matching show that using the HSO algorithm as the optimization method in the assisted history matching workflow improves the simulation quality and saves solution time significantly.
文摘The growing demands for high speed connectivity to keep pace with bandwidth intensive applications and services have spawned the idea of developing PONs with capabilities beyond those of copper and wireless-based technologies in access network. In this article, an approach for the design of an energy efficient bandwidth allocation mechanism for the shared upstream communication link in the Fiber to the Home (FTTH) access network is presented and evaluated using Mixed Integer Linear Programming (MILP) model. In the MILP model, two objective functions for minimization of power consumption and minimization of blocking were evaluated. The results have shown that with the objective of power minimization approach, Optical Network Terminals (ONTs) are efficiently grouped to the minimum number of active networking Optical Line Terminal (OLT) switches, traffic is groomed, ports are efficiently utilized, and hence total power consumption is minimized. Results have shown that with energy efficient bandwidth allocation approach consideration, energy savings can reach up to 80% for different examined traffic loads following uniform distribution.
文摘It is demonstrated that the recently introduced semantic intelligence spontaneously maintains bounded logical and quantal error on each and every semantic trajectory, unlike its algorithmic counterpart which is not able to. This result verifies the conclusion about the assignment of equal evolutionary value to the motion on the set of all the semantic trajectories sharing the same homeostatic pattern. The evolutionary value of permanent and spontaneous maintenance of boundedness of logical and quantal error on each and every semantic trajectory is to make available spontaneous maintenance of the notion of a kind intact in the long run.
文摘Aimed at improving the insufficient search ability of constraint differential evolution with single constraint handling technique when solving complex optimization problem, this paper proposes a constraint differential evolution algorithm?based on ensemble of constraint handling techniques and multi-population?framework, called ECMPDE. First, handling three improved variants of differential evolution algorithms are dynamically matched with two constraint handling techniques through the constraint allocation mechanism. Each combination includes three variants with corresponding constraint handling technique?and these combinations are in the set. Second, the population is divided into three smaller subpopulations and one larger reward subpopulation. Then a combination with three constraint algorithms is randomly selected from the set, and the three constraint algorithms are run in three sub-populations respectively. According to the improvement of fitness value, the optimal constraint?algorithm is selected to run on the reward sub-population, which can share?information and close cooperation among populations. In order to verify the effectiveness of the proposed algorithm, 12 standard constraint optimization problems?and 10 engineering constraint optimization problems are tested. The experimental results show that ECMPDE is an effective algorithm for solving constraint optimization problems.
文摘This study presents an optimization technique and design for a stand-alone photovoltaic (PV) system to provide the required electricity for a single residential household in remote areas. From the basic solar components analysis, the irradiance on tilted surface is derived and compared to that on horizontal surface for Furu-Awa locality to infer the appropriate tilt angle (β) that maximizes the collection of solar energy. Seven optimum values of β applicable to the PV network were then derived depending of the period of the year and this simulation resulted that the panels are to be adjusted seven times a year. The optimization technique for load demand based on total apparent power of the household appliances produces an increase of 18% compared to the simple case of the PV components design using active power but leads to the optimum configuration that meets the real load demand of the household. Following the sizing of the station, reliability tests simulations were conducted for a one year corresponding period to infer the sensitivity of power supply to initial state of charge, to check the system autonomy and to evaluate the effect of random variation of the load on the smooth functioning of the PV system using a pseudo random number generator. This analysis shows that the minimum capacity of the battery for normal run of the Plan is 22.2% and that with random fluctuation of load, there will be periods of the year where the system experiences power failure depending on how important is the variation. The result of the study may imply a small increase in the cost of the entire plant but improves the stability and flexibility of such a station.
基金supported by the National Natural Science Foundation of China(Grant Nos.51779262,51506220 and 51579244)。
文摘This review paper summarizes constructal design progress performed by the authors for eight types of heat sinks with ten performance indexes being taken as the optimization objectives,respectively,by combining the methods of theoretical analysis and numerical calculation.The eight types of heat sinks are uniform height rectangular fin heat sink,non-uniform height rectangular fin heat sink,inline cylindrical pin-fin heat sink(ICPHS),plate single-row pin fin heat sink(PSRPHS),plate inline pin fin heat sink(PIPHS),plate staggered pin fin heat sink(PSPHS),single-layered microchannel heat sink(SLMCHS)with rectangular cross sections and double-layered microchannel heat sink(DLMCHS)with rectangular cross sections,respectively.And the ten performance indexes are heat transfer rate maximization,maximum thermal resistance minimization,minimization of equivalent thermal resistance which is defined based on the entransy dissipation rate(equivalent thermal resistance for short),field synergy number maximization,entropy generation rate minimization,operation cost minimization,thermo-economic function value minimization,pressure drop minimization,enhanced heat transfer factor maximization and efficiency evaluation criterion number maximization,respectively.The optimal constructs of the eight types of heat sinks with different constraints and based on the different optimization objectives are compared with each other.The results indicated that the optimal constructs mostly are different based on different optimization objectives under the same boundary condition.The optimization objective should be suitable chosen based on the focus when the constructal design for one heat sink is performed.The results obtained herein have some important theoretical significances and application values,and can provide scientific bases and theoretical guidelines for the thermal design of real heat sinks and their applications.
基金the Assets4Rail Project which is funded by the Shift2Rail Joint Undertaking under the EU’s H2020 program(Grant No.826250)the Open Research Fund of State Key Laboratory of Traction Power of Southwest Jiaotong University(Grant No.TPL2011)+1 种基金part of the experiment data concerning the railway line is supported by the DynoTRAIN Project,funded by European Commission(Grant No.234079)The first author is also supported by the China Scholarship Council(Grant No.201707000113).
文摘The existing multi-objective wheel profile optimization methods mainly consist of three sub-modules:(1)wheel profile generation,(2)multi-body dynamics simulation,and(3)an optimization algorithm.For the first module,a comparably conservative rotary-scaling finetuning(RSFT)method,which introduces two design variables and an empirical formula,is proposed to fine-tune the traditional wheel profiles for improving their engineering applicability.For the second module,for the TRAXX locomotives serving on the Blankenburg–Rubeland line,an optimization function representing the relationship between the wheel profile and the wheel–rail wear number is established based on Kriging surrogate model(KSM).For the third module,a method combining the regression capability of KSM with the iterative computing power of particle swarm optimization(PSO)is proposed to quickly and reliably implement the task of optimizing wheel profiles.Finally,with the RSFT–KSM–PSO method,we propose two wear-resistant wheel profiles for the TRAXX locomotives serving on the Blankenburg–Rubeland line,namely S1002-S and S1002-M.The S1002-S profile minimizes the total wear number by 30%,while the S1002-M profile makes the wear distribution more uniform through a proper sacrifice of the tread wear number,and the total wear number is reduced by 21%.The quasi-static and hunting stability tests further demonstrate that the profile designed by the RSFT–KSM–PSO method is promising for practical engineering applications.
文摘This paper presents new hybrid methods for the identification of optimal topologies by combining the teaching-learning based optimization(TLBO)and the method of moving asymptotes(MMA).The topology optimization problem is parameterizing with a low dimensional explicit method called moving morphable components(MMC),to make the use of evolutionary algorithms more efficient.Gradient-based solvers have good performance in solving large-scale topology optimization problems.However,in unconventional cases same as crashworthiness design in which there is numerical noise in the gradient information,the uses of these algorithms are unsuitable.The standard evolutionary algorithms can solve such problems since they don’t need gradient information.However,they have a high computational cost.This paper is based upon the idea of combining metaheuristics with mathematical programming to handle the probable noises and have faster convergence speed.Due to the ease of computations,the compliance minimization problem is considered as the case study and the artificial noise is added in gradient information.
基金National Key Research Program of China(No.2017YFC0803801)。
文摘The laser scanning system based on Simultaneous Localization and Mapping(SLAM)technology has the advantages of low cost,high precision and high efficiency.It has drawn wide attention in the field of surveying and mapping in recent years.Although real-time data acquisition can be achieved using SLAM technology,the precision of the data can’t be ensured,and inconsistency exists in the acquired point cloud.In order to improve the precision of the point cloud obtained by this kind of system,this paper presents a hierarchical point cloud global optimization algorithm.Firstly,the“point-to-plane”iterative closest point(ICP)algorithm is used to match the overlapping point clouds to form constraints between the trajectories of the scanning system.Then a pose graph is constructed to optimize the trajectory.Finally,the optimized trajectory is used to refine the point cloud.The computational efficiency is improved by decomposing the optimization process into two levels,i.e.local level and global level.The experimental results show that the RMSE of the distance between the corresponding points in overlapping areas is reduced by about 50%after optimization,and the internal inconsistency is effectively eliminated.
文摘A comprehensive mathematical model is developed to simulate the interactions of the complex processes that take place in typical catalytic chemical reactors. This mathematical model includes correlations representing various modes of mass transport and chemical reactions. To illustrate the application and value of this approach for reactor optimizations, the model is applied to the case of series reactions with a desirable intermediate compound and the risk of degradation of this compound if the process conditions are not optimized. The modeling results show that in such cases, which are very common in practice, replacing the conventional uniform catalyst distribution with a novel non-uniform distribution will significantly improve the performance of the reactor and the production of the desirable compound. Various catalyst distribution options are compared, and a novel non-uniform loading of catalyst is identified that gives a much better performance compared to the conventional approach. The model is versatile and useful for both the design as well as the optimization of the catalytic fixed-bed reactors in a wide variety of reactor and reaction conditions.
基金the National Natural Science Founda-tion of China(Nos.51965034 and 51565028)LanzhouTalent Innovation and Entrepreneurship Project(No.2018-RC-25)。
文摘The 2MW wind turbine tower is considered as the baseline configuration for structural optimization.The design variables consist of the thickness and height located at the top tower junction.The relationships between the design variables and the optimization objectives(mass,equivalent stress,top displacement and fatigue life)are mapped on the basis of uniform design and regression analysis.Subsequently,five solutions are developed by an algorithm,NSGA-III.According to their efficiency and applicability,the most suitable solution is found.This approach yields a decrease of 0.48%in the mass,a decrease of 54.48%in the equivalent stress and an increase of 8.14%in fatigue life,as compared with existing tower designs.An improved wind turbine tower is obtained for this practice.
文摘With the widespread application of distributed systems, many problems need to be solved urgently. How to design distributed optimization strategies has become a research hotspot. This article focuses on the solution rate of the distributed convex optimization algorithm. Each agent in the network has its own convex cost function. We consider a gradient-based distributed method and use a push-pull gradient algorithm to minimize the total cost function. Inspired by the current multi-agent consensus cooperation protocol for distributed convex optimization algorithm, a distributed convex optimization algorithm with finite time convergence is proposed and studied. In the end, based on a fixed undirected distributed network topology, a fast convergent distributed cooperative learning method based on a linear parameterized neural network is proposed, which is different from the existing distributed convex optimization algorithms that can achieve exponential convergence. The algorithm can achieve finite-time convergence. The convergence of the algorithm can be guaranteed by the Lyapunov method. The corresponding simulation examples also show the effectiveness of the algorithm intuitively. Compared with other algorithms, this algorithm is competitive.
文摘This work concerns an experimental and numerical study of energy losses in a typical oven usually used in the agro-food craft sector in Burkina Faso. The experimental results were obtained by infrared thermography of the oven and by monitoring the evolution of the wall temperatures using thermocouples connected to a data acquisition system. These results indicate that the energy losses are mainly through the walls of the oven. The numerical study based on the energy balance and corroborated by the experimental study made it possible to quantify these losses of energy which represents almost half of the fuel used. These results will allow us to work on a new, more efficient oven model for the grilling sector in Burkina Faso.
文摘Efficient tuning of the coefficients used by proportional-integral-derivative(PID)controllers enhances their performance.For highly non-linear systems,optimization algorithms are required to make the PID controllers more responsive to disturbances.The production of tert-amyl-methyl-ether(TAME),an essential additive for gasoline,in reactive distillation columns integrates highly non-linear reaction and separation processes.On the other hand,TAME distillation is an azeotrope distillation process,therefore non-linearity of this process is more complex than that of conventional distillation.PID-controller tuning methods applying a genetic algorithm(GA)and a particle swarm optimization(PSO)algorithm are compared using a dynamic simulation that integrates the optimization algorithms with the HYSYS process simulator.The PID controller response trends are analyzed following the introduction of a significant disturbance to the TAME reactive distillation column(i.e.,a ten percent change in the methanol feed temperature).The PSO PID controller tuning method that minimizes the integral of the absolute error(IAE)as its objective function significantly outperforms the GA tuning method.The novel PID-tuning methodology developed has more extensive application potential.