Maintenance decision problems generally involve multiple criteria which apparently are best addressed using Multi-Criteria Decision Making (MCDM) tools. This paper describes the use of a hybrid MCDM technique in prior...Maintenance decision problems generally involve multiple criteria which apparently are best addressed using Multi-Criteria Decision Making (MCDM) tools. This paper describes the use of a hybrid MCDM technique in prioritizing maintenance strategy for ship systems. The Hybrid MCDM technique combines Delphi method, AHP and TOPSIS methods. While the Delphi method and AHP are applied in screening of decision criteria and decision criteria weights determination respectively, the TOPSIS method is used in the ranking of alternative maintenance strategies. Five alternative maintenance strategies which include corrected maintenance, scheduled overhaul, scheduled replacement, continuous on-condition task and scheduled on-condition task are considered and the optimum maintenance strategy is selected based on twelve critical maintenance decision criteria. To demonstrate the suitability of the approach a case study of sea water pump of the central cooling system of a marine diesel engine is used.展开更多
In the context of food security,drying is a crucial postharvest process for paddy grain because it significantly impacts the quality of both paddy and rice.To conserve energy during the drying process,deep bed dryers ...In the context of food security,drying is a crucial postharvest process for paddy grain because it significantly impacts the quality of both paddy and rice.To conserve energy during the drying process,deep bed dryers are used as convective dryers that use a combination of ambient airflow and heating,thus relying on airflow,temperature,and relative humidity(RH)as the primary drying parameters.Consequently,an aeration system is necessary so that the drying air can penetrate the thick pile of paddy grain and distribute evenly throughout the drying chamber.This analysis aimed to determine the most optimal aeration system by using computational fluid dynamics(CFD)and the AHP-TOPSIS method.The quantitative and visual analysis of the airflow velocity,pressure,temperature,and RH was conducted using CFD on four different dryer aeration systems models,which were then ranked by preference value using the AHP-TOPSIS method.Model 4,with a sloping floor and circular pipe formation,was found to have the most optimal aeration system(preference value of 0.788)for a paddy grain deep bed dryer prototype.展开更多
为科学评估水电站与抽水蓄能联合运行的综合性能,提出一种基于层次分析法(analytic hierarchy process,AHP)与逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)相结合的多指标综合评价模...为科学评估水电站与抽水蓄能联合运行的综合性能,提出一种基于层次分析法(analytic hierarchy process,AHP)与逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)相结合的多指标综合评价模型。首先,从经济性、技术性、环境效益、水情及政策5个维度出发,构建包含发电效率、调峰能力、碳排放强度、来水量变化等11项指标的综合评价体系。其次,采用AHP确定各指标权重以体现不同维度的差异性影响,并通过TOPSIS计算各运行方案与理想解的贴近度,实现联合运行模式的优劣排序。最后,以某区域水电站与抽水蓄能联合工程为实例进行验证分析。结果表明:相较于传统单一评价方法,AHP-TOPSIS模型能够有效兼顾主客观因素,量化评价结果,其中调峰能力与动态投资回收期对综合性能影响显著;同时,联合运行方案中储能容量配置与调度策略的协同优化可提升系统综合效益15%以上。研究结果为多能互补系统中水电-抽蓄联合运行的方案优选与决策制定提供了理论依据,对推动清洁能源高效利用具有实际意义。展开更多
In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to ...In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.展开更多
In this paper,a novel method for investigating the particle-crushing behavior of breeding particles in a fusion blanket is proposed.The fractal theory and Weibull distribution are combined to establish a theoretical m...In this paper,a novel method for investigating the particle-crushing behavior of breeding particles in a fusion blanket is proposed.The fractal theory and Weibull distribution are combined to establish a theoretical model,and its validity was verified using a simple impact test.A crushable discrete element method(DEM)framework is built based on the previously established theoretical model.The tensile strength,which considers the fractal theory,size effect,and Weibull variation,was assigned to each generated particle.The assigned strength is then used for crush detection by comparing it with its maximum tensile stress.Mass conservation is ensured by inserting a series of sub-particles whose total mass was equal to the quality loss.Based on the crushable DEM framework,a numerical simulation of the crushing behavior of a pebble bed with hollow cylindrical geometry under a uniaxial compression test was performed.The results of this investigation showed that the particle withstands the external load by contact and sliding at the beginning of the compression process,and the results confirmed that crushing can be considered an important method of resisting the increasing external load.A relatively regular particle arrangement aids in resisting the load and reduces the occurrence of particle crushing.However,a limit exists to the promotion of resistance.When the strain increases beyond this limit,the distribution of the crushing position tends to be isotropic over the entire pebble bed.The theoretical model and crushable DEM framework provide a new method for exploring the pebble bed in a fusion reactor,considering particle crushing.展开更多
Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts.This paper proposes a novel partitioning method based on deep reinforcement learning.First,the partitioning decision...Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts.This paper proposes a novel partitioning method based on deep reinforcement learning.First,the partitioning decision process is formulated as a Markov decision process(MDP)model to maximize the modularity.Corresponding key partitioning constraints on parallel restoration are considered.Second,based on the partitioning objective and constraints,the reward function of the partitioning MDP model is set by adopting a relative deviation normalization scheme to reduce mutual interference between the reward and penalty in the reward function.The soft bonus scaling mechanism is introduced to mitigate overestimation caused by abrupt jumps in the reward.Then,the deep Q network method is applied to solve the partitioning MDP model and generate partitioning schemes.Two experience replay buffers are employed to speed up the training process of the method.Finally,case studies on the IEEE 39-bus test system demonstrate that the proposed method can generate a high-modularity partitioning result that meets all key partitioning constraints,thereby improving the parallelism and reliability of the restoration process.Moreover,simulation results demonstrate that an appropriate discount factor is crucial for ensuring both the convergence speed and the stability of the partitioning training.展开更多
文摘Maintenance decision problems generally involve multiple criteria which apparently are best addressed using Multi-Criteria Decision Making (MCDM) tools. This paper describes the use of a hybrid MCDM technique in prioritizing maintenance strategy for ship systems. The Hybrid MCDM technique combines Delphi method, AHP and TOPSIS methods. While the Delphi method and AHP are applied in screening of decision criteria and decision criteria weights determination respectively, the TOPSIS method is used in the ranking of alternative maintenance strategies. Five alternative maintenance strategies which include corrected maintenance, scheduled overhaul, scheduled replacement, continuous on-condition task and scheduled on-condition task are considered and the optimum maintenance strategy is selected based on twelve critical maintenance decision criteria. To demonstrate the suitability of the approach a case study of sea water pump of the central cooling system of a marine diesel engine is used.
基金funded by Balai Pembiayaan Pendidikan Tinggi,Kemendikbudristek,and Lembaga Pengelola Dana Pendidikan(LPDP)through the Indonesian Education Scholarship(1083/J5.2.3/BPI.06/10/2021)supported by Prof.Samsul Rizal of the Department of Mechanical and Industrial Engineering of Universitas Syiah Kuala in the application of Ansys software,which was funded by the LPDP and managed by Indonesian Science Fund(RISPRO/KI/B1/TKL/5/15448/2020)。
文摘In the context of food security,drying is a crucial postharvest process for paddy grain because it significantly impacts the quality of both paddy and rice.To conserve energy during the drying process,deep bed dryers are used as convective dryers that use a combination of ambient airflow and heating,thus relying on airflow,temperature,and relative humidity(RH)as the primary drying parameters.Consequently,an aeration system is necessary so that the drying air can penetrate the thick pile of paddy grain and distribute evenly throughout the drying chamber.This analysis aimed to determine the most optimal aeration system by using computational fluid dynamics(CFD)and the AHP-TOPSIS method.The quantitative and visual analysis of the airflow velocity,pressure,temperature,and RH was conducted using CFD on four different dryer aeration systems models,which were then ranked by preference value using the AHP-TOPSIS method.Model 4,with a sloping floor and circular pipe formation,was found to have the most optimal aeration system(preference value of 0.788)for a paddy grain deep bed dryer prototype.
文摘为科学评估水电站与抽水蓄能联合运行的综合性能,提出一种基于层次分析法(analytic hierarchy process,AHP)与逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)相结合的多指标综合评价模型。首先,从经济性、技术性、环境效益、水情及政策5个维度出发,构建包含发电效率、调峰能力、碳排放强度、来水量变化等11项指标的综合评价体系。其次,采用AHP确定各指标权重以体现不同维度的差异性影响,并通过TOPSIS计算各运行方案与理想解的贴近度,实现联合运行模式的优劣排序。最后,以某区域水电站与抽水蓄能联合工程为实例进行验证分析。结果表明:相较于传统单一评价方法,AHP-TOPSIS模型能够有效兼顾主客观因素,量化评价结果,其中调峰能力与动态投资回收期对综合性能影响显著;同时,联合运行方案中储能容量配置与调度策略的协同优化可提升系统综合效益15%以上。研究结果为多能互补系统中水电-抽蓄联合运行的方案优选与决策制定提供了理论依据,对推动清洁能源高效利用具有实际意义。
基金Supported in part by Natural Science Foundation of Guangxi(2023GXNSFAA026246)in part by the Central Government's Guide to Local Science and Technology Development Fund(GuikeZY23055044)in part by the National Natural Science Foundation of China(62363003)。
文摘In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.
基金supported by Anhui Provincial Natural Science Foundation(2408085QA030)Natural Science Research Project of Anhui Educational Committee,China(2022AH050825)+3 种基金Medical Special Cultivation Project of Anhui University of Science and Technology(YZ2023H2C008)the Excellent Research and Innovation Team of Anhui Province,China(2022AH010052)the Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology,China(2021yjrc51)Collaborative Innovation Program of Hefei Science Center,CAS,China(2019HSC-CIP006).
文摘In this paper,a novel method for investigating the particle-crushing behavior of breeding particles in a fusion blanket is proposed.The fractal theory and Weibull distribution are combined to establish a theoretical model,and its validity was verified using a simple impact test.A crushable discrete element method(DEM)framework is built based on the previously established theoretical model.The tensile strength,which considers the fractal theory,size effect,and Weibull variation,was assigned to each generated particle.The assigned strength is then used for crush detection by comparing it with its maximum tensile stress.Mass conservation is ensured by inserting a series of sub-particles whose total mass was equal to the quality loss.Based on the crushable DEM framework,a numerical simulation of the crushing behavior of a pebble bed with hollow cylindrical geometry under a uniaxial compression test was performed.The results of this investigation showed that the particle withstands the external load by contact and sliding at the beginning of the compression process,and the results confirmed that crushing can be considered an important method of resisting the increasing external load.A relatively regular particle arrangement aids in resisting the load and reduces the occurrence of particle crushing.However,a limit exists to the promotion of resistance.When the strain increases beyond this limit,the distribution of the crushing position tends to be isotropic over the entire pebble bed.The theoretical model and crushable DEM framework provide a new method for exploring the pebble bed in a fusion reactor,considering particle crushing.
基金funded by the Beijing Engineering Research Center of Electric Rail Transportation.
文摘Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts.This paper proposes a novel partitioning method based on deep reinforcement learning.First,the partitioning decision process is formulated as a Markov decision process(MDP)model to maximize the modularity.Corresponding key partitioning constraints on parallel restoration are considered.Second,based on the partitioning objective and constraints,the reward function of the partitioning MDP model is set by adopting a relative deviation normalization scheme to reduce mutual interference between the reward and penalty in the reward function.The soft bonus scaling mechanism is introduced to mitigate overestimation caused by abrupt jumps in the reward.Then,the deep Q network method is applied to solve the partitioning MDP model and generate partitioning schemes.Two experience replay buffers are employed to speed up the training process of the method.Finally,case studies on the IEEE 39-bus test system demonstrate that the proposed method can generate a high-modularity partitioning result that meets all key partitioning constraints,thereby improving the parallelism and reliability of the restoration process.Moreover,simulation results demonstrate that an appropriate discount factor is crucial for ensuring both the convergence speed and the stability of the partitioning training.
