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The shaftless rim thruster has an important role underwater construction in China, and its electromagnetic drive part uses a large diameter inner rotor permanent magnet brushless DC motor structure. The permanent magnet brushless DC motor structure of the electromagnetic drive part of the shaftless rim thruster was designed and the motor performance optimization analysis was carried out based on Maxwell to improve its performance. Four parameters such as efficiency and starting current were selected as the optimization objectives, and six parameters related to the slot type were selected as the optimization variables. The back propagation( BP) neural network was used to establish the relationship between the optimization variables and the optimization target. The linear weighting method was used to determine the fitness function to perform a multi-objective optimization analysis of the motor to improve its performance. Finally, the optimal solution was substituted back to the Maxwell model to verify the validity of the solution results. The results show that the performance of the motor is improved after optimization, and the optimization results are fine and achieve the expected results. [ABSTRACT FROM AUTHOR]

The machinery manufacturing industry has always been an important pillar of China's economic development. When we solve different problems in mechanical production, people often face the trade-off of having to choose among some desired multiple optimization goals, because many goals cannot often be optimized at the same time, or even conflict with each other. To improve the comprehensive performance of hydrodynamic sliding bearing, the improved multi-objective model is applied to this problem to obtain more reasonable parameter design. We use MOPSO with linear weighting method to solve our proposed model. The results of a numerical example show that our model and algorithm are feasible and more effective than the traditional MOPSO by more reasonable parameter design. [ABSTRACT FROM AUTHOR]

Climate variation and underlying surface dynamics have caused a significant change in the trend of evapotranspiration (ET) in the Yellow River Basin (YRB) over the last two decades. Combined with the measured rainfall, runoff and gravity recovery and climate experiment (GRACE) product, five global ET products were firstly merged using a linear weighting method. Linear slope, “two-step” multiple regression, partial differential, and residual methods were then employed to explore the quantitative impacts of precipitation (PCPN), temperature (Temp), sunshine duration (SD), vapor pressure deficit (VPD), wind speed (WS), leaf area index (LAI), and the residual factors (e.g., microtopography changes, irrigation, etc.) on the ET trend in the YRB. The results show that: (1) The ET estimates were improved by merging five global ET products using the linear weighting method. The sensitivities of climatic factors and LAI on the ET trend can be separately calculated using proposed “two-step” statistical regression method; (2) the overall ET trend in the entire study area during 2000–2018 was 3.82 mm/yr, and the highest ET trend was observed in the Toudaoguai-Longmen subregion. ET trend was dominantly driven by vegetation greening, with an impact of 2.47 mm/yr and a relative impact rate of 51.16%. The results indicated that the relative impact rate of the residual factors (e.g., microtopography, irrigation, etc.) on the ET trend is up to 28.17%. The PCPN and VPD had increasing roles on the ET trend, with impacts of 0.45 mm/yr and 0.05 mm/yr, respectively, whereas the Temp, SD, and WS had decreasing impacts of –0.19 mm/yr, –0.15 mm/yr, and –0.17 mm/yr, respectively. (3) The spatial pattern of impact of specific influencing factor on the ET trend was determined by the spatial pattern of change trend slope of this factor and sensitivity of ET to this factor. ET trends of the source area and the Qingtongxia–Toudaoguai were dominated by the climatic factors, while the residual factors dominated the ET trend in the Tangnaihai–Qingtongxia area. The vegetation restoration was the dominant factor causing the increase in the ET in the middle reaches of the YRB, and the impact rates of the LAI were ranked as follows: Yanhe Rive > Wudinghe River > Fenhe River > Jinghe River > Beiluohe River > Qinhe River > Kuyehe River > Yiluohe River.

Regional integrated energy site layout optimization involves multi-energy coupling, multi-data processing and multi-objective decision making, among other things. It is essentially a kind of non-convex multi-objective nonlinear programming problem, which is very difficult to solve by traditional methods. This paper proposes a decentralized optimization and comprehensive decision-making planning strategy and preprocesses the data information, so as to reduce the difficulty of solving the problem and improve operational efficiency. Three objective functions, namely the number of energy stations to be built, the coverage rate and the transmission load capacity of pipeline network, are constructed, normalized by linear weighting method, and solved by the improved p-median model to obtain the optimal value of comprehensive benefits. The artificial immune algorithm was improved from the three aspects of the initial population screening mechanism, population updating and bidirectional crossover-mutation, and its performance was preliminarily verified by test function. Finally, an improved artificial immune algorithm is used to solve and optimize the regional integrated energy site layout model. The results show that the strategies, models and methods presented in this paper are feasible and can meet the interest needs and planning objectives of different decision-makers.

In order to simplify the emergency rescue expert selection process after the emergency rescue hazard, combined with the characteristics of emergency rescue decision-making, this paper analyzed the evaluation indexes that should be considered in evaluating the level of emergency rescue experts in the same field. Firstly, the evaluation index system of emergency rescue experts in the same field is established including the social attribute, academic attribute, decision attribute, work attribute four level evaluation index and eleven secondary evaluation index. Secondly, the evaluation index assignment and quantization formula are put forward. Based on the linear weighting method, the quantitative model of emergency rescue experts in the same field is constructed. Finally, the rationality of the quantitative model is verified by an example. The results show that the quantitative model is feasible and practical, and it can realize the quantification of the experts in the same field, and can sort the experts in the same field according to the quantitative results. [ABSTRACT FROM AUTHOR]

Summary The terrestrial water budget over northern China, which plays an important role in water resource management, has experienced great changes during the past decades. However, its spatiotemporal variations in the past calculated from individual datasets remain quite uncertain. In this study, we improve the accuracy of evapotranspiration ( E ), precipitation ( P ) and runoff ( R ) estimates by merging remote sensing, reanalysis, data assimilation datasets and ground observations, and further analyze the spatiotemporal characterization of the terrestrial water budget at 0.25° over northern China during the period of 1984–2010. The results illustrate that using any of the individual datasets, there is significant uncertainty and an obvious seasonal cycle in the terrestrial water budget. Large differences exist among the different datasets, and the merged E , P and R outperform the individual datasets. The root mean square errors ( RMSE s) from cross-validation are 8.4–14.2 mm, 15.9–27.3 mm and 4.1–14.2 mm for the monthly merged E , P and R at the site scale of the different basins, respectively. The spatial patterns of the merged annual E and R are consistent with that of P due to the water limitations mainly controlled by P . The interannual variations in these hydrological variables indicate a slight increase in the variables from 1984 to 1998, with a large El Niño event, and a larger decline thereafter as a result of a large-scale drought. However, decadal trends in terrestrial water storage changes ( TWSC ) over all five basins inferred from the merged products tend to increase to some extent with climate warming over the studied time period. The Budyko curve reveals that an increase in vegetation coverage increases the evaporation ratio ( E / P ) to some extent, but climate change is the dominant driver for the variations in the hydrological variables in these regions. [ABSTRACT FROM AUTHOR]

Climate variation and underlying surface dynamics have caused a significant change in the trend of evapotranspiration (ET) in the Yellow River Basin (YRB) over the last two decades. Combined with the measured rainfall, runoff and gravity recovery and climate experiment (GRACE) product, five global ET products were firstly merged using a linear weighting method. Linear slope, "two-step" multiple regression, partial differential, and residual methods were then employed to explore the quantitative impacts of precipitation (PCPN), temperature (Temp), sunshine duration (SD), vapor pressure deficit (VPD), wind speed (WS), leaf area index (LAI), and the residual factors (e.g., microtopography changes, irrigation, etc.) on the ET trend in the YRB. The results show that: (1) The ET estimates were improved by merging five global ET products using the linear weighting method. The sensitivities of climatic factors and LAI on the ET trend can be separately calculated using proposed "two-step" statistical regression method; (2) the overall ET trend in the entire study area during 2000–2018 was 3.82 mm/yr, and the highest ET trend was observed in the Toudaoguai-Longmen subregion. ET trend was dominantly driven by vegetation greening, with an impact of 2.47 mm/yr and a relative impact rate of 51.16%. The results indicated that the relative impact rate of the residual factors (e.g., microtopography, irrigation, etc.) on the ET trend is up to 28.17%. The PCPN and VPD had increasing roles on the ET trend, with impacts of 0.45 mm/yr and 0.05 mm/yr, respectively, whereas the Temp, SD, and WS had decreasing impacts of –0.19 mm/yr, –0.15 mm/yr, and –0.17 mm/yr, respectively. (3) The spatial pattern of impact of specific influencing factor on the ET trend was determined by the spatial pattern of change trend slope of this factor and sensitivity of ET to this factor. ET trends of the source area and the Qingtongxia–Toudaoguai were dominated by the climatic factors, while the residual factors dominated the ET trend in the Tangnaihai–Qingtongxia area. The vegetation restoration was the dominant factor causing the increase in the ET in the middle reaches of the YRB, and the impact rates of the LAI were ranked as follows: Yanhe Rive > Wudinghe River > Fenhe River > Jinghe River > Beiluohe River > Qinhe River > Kuyehe River > Yiluohe River. [ABSTRACT FROM AUTHOR]

• A novel distributed framework is proposed to solve the multi-objective problem. • The whole process is independent for each agent only needs its own information. • A problem in micro-grid is proposed to prove the effectiveness of this framework. In this paper, a novel distributed framework is proposed to solve the multi-objective problem which composed of several conflicting objective functions. In order to overcome the impact of inconsistent units of measurement for each objective function, a normalized function is applied to eliminate it. It will split a multi-objective optimization problem into three subproblems: the problem of maximization, the problem of minimization, the problem after normalization. Linear weighting method is adopted to assign weights to each normalized objective function and convert them into a single objective problem. Specifically, each objective function which regarded as an agent will calculate its maximum and minimum values respectively and finally the Pareto optimal solution is obtained through a distributed neuro-dynamic algorithm. The whole process is distributed and independent for each agent only needs its own rather than the global information. Furthermore, the convergence of the proposed algorithm can be proved through Lyapunov function. Eventually, a numerical example and a multi-objective optimization problem in micro grid are used to prove the credibility of the framework. [ABSTRACT FROM AUTHOR]

Regional integrated energy site layout optimization involves multi-energy coupling, multi-data processing and multi-objective decision making, among other things. It is essentially a kind of non-convex multi-objective nonlinear programming problem, which is very difficult to solve by traditional methods. This paper proposes a decentralized optimization and comprehensive decision-making planning strategy and preprocesses the data information, so as to reduce the difficulty of solving the problem and improve operational efficiency. Three objective functions, namely the number of energy stations to be built, the coverage rate and the transmission load capacity of pipeline network, are constructed, normalized by linear weighting method, and solved by the improved p-median model to obtain the optimal value of comprehensive benefits. The artificial immune algorithm was improved from the three aspects of the initial population screening mechanism, population updating and bidirectional crossover-mutation, and its performance was preliminarily verified by test function. Finally, an improved artificial immune algorithm is used to solve and optimize the regional integrated energy site layout model. The results show that the strategies, models and methods presented in this paper are feasible and can meet the interest needs and planning objectives of different decision-makers. [ABSTRACT FROM AUTHOR]