On a universal model for the prediction of the daily global solar radiation

A model to predict the mean expected daily global solar radiation, H(n) on a day n, at a site with latitude φ is proposed. The model is based on two cosine functions. A regression analysis taking into account the mean measured values H_m.meas(n) obtained from SoDa database for 42 sites in the Northern Hemisphere resulted in a set of mathematical expressions of split form to predict H(n). The parameters of the two cosine model for 0°<φ < 23° are obtained by regression analysis using a sum of 3–8 Gaussian functions, while for 23°<φ < 71° the two cosine model parameters are expressed by a sum of exponential functions or the product of an exponential and a cosine function. The main equation of the model and the set of parametric expressions provide H(n) for any φ on Earth. Validation results of this model are provided along with the statistical estimators NMBE, NRMSE and t-statistic in comparison to the corresponding values from three databases of NASA, SoDa and the measured values from ground stations provided in Meteonorm.     

Storage of hybrid rough rice – Consideration of microbial growth kinetics and prediction models

The objective of this study was to determine kinetics of mold growth on popularly grown hybrid long-grain rough rice during storage at conditions that simulate delayed drying and prolonged storage. Three long-grain hybrid rice cultivars, XL753 (2014) and CL XL745 (2014, 2015) and XL760 (2015) conditioned to four different moisture contents (MCs) (12.5%, 16.0%, 19.0%, and 21.0% wet basis) were stored in rough rice form at temperatures ranging from 10 °C to 40 °C for a period of 16 weeks. The study was repeated using rice from two consecutive crop seasons, 2014 and 2015. For all cultivars, a direct relationship between mold counts and rice MC was observed – whereas more complex trends were observed for the effect of temperature and the duration of storage on mold growth. Kinetic models including Baranyi, Weibull, Gompertz, Richard and Buchanan were successfully modified and fitted using non-linear regression and used to predict the mold counts (log₁₀ CFU/g) for varying conditions (correlations = 0.65–0.76). The study concluded that long-grain hybrid rough rice could be stored at low MC levels (≤17%) and moderate temperatures (≤27 °C) for up to 6 weeks without any change in the mold growth profile. However, storing rice at high MC (>17%) for more than 8 weeks, especially at higher (>27 °C) temperatures should be avoided due to the potential for high mold activity leading to loss of the grain quality.     

Reliability matrix solution to multiple mechanism prediction

We present a method for predicting the failure rate and thus the reliability of an electronic system by summing the failure rate of each known failure mechanism. We use a competing acceleration factor methodology by combining the physics of failure for each mechanism with its own effect as observed by High/Low temperature and High/Low voltage stresses. Our Multiple High Temperature Overstress Life-test (M-HTOL) method assumes that the lifetime of each failure mechanism follows constant rate distribution whereby each mechanism is independently accelerated by its own stress factors. Stresses include temperature, frequency, current, and other factors that can be entered into a reliability model. The overall failure rate thus, also follows an exponential distribution and is described as the standard FIT (Failure unIT or Failure in Time). This method combines mathematical models for known failure mechanism and solves them simultaneously for a multiplicity of accelerated life test results to find a consistent set of weighting factors for each mechanism. The result of solving the system of equations is a more accurate and a unique combination for each system model by proportional summation of each of the contributing failure mechanisms.     

A multi-objective evolutionary algorithm guided by directed search for dynamic scheduling

This paper considers dynamic multi-objective machine scheduling problems in response to continuous arrival of new jobs, under the assumption that jobs can be rejected and job processing time is controllable. The operational cost and the cost of deviation from the baseline schedule need to be optimized simultaneously. To solve these dynamic scheduling problems, a directed search strategy (DSS) is introduced into the elitist non-dominated sorting genetic algorithm (NSGA-II) to enhance its capability of tracking changing optimums while maintaining fast convergence. The DSS consists of a population re-initialization mechanism (PRM) to be adopted upon the arrival of new jobs and an offspring generation mechanism (OGM) during evolutionary optimization. PRM re-initializes the population by repairing the non-dominated solutions obtained before the disturbances occur, modifying randomly generated solutions according to the structural properties, as well as randomly generating solutions. OGM generates offspring individuals by fine-tuning a few randomly selected individuals in the parent population, employing intermediate crossover in combination with Gaussian mutations to generate offspring, and using intermediate crossover together with a differential evolution based mutation operator. Both PRM and OGM aim to strike a good balance between exploration and exploitation in solving the dynamic multi-objective scheduling problem. Comparative studies are performed on a variety of problem instances of different sizes and with different changing dynamics. Experimental results demonstrate that the proposed DSS is effective in handling the dynamic scheduling problems under investigation.     

Regressor selection for ozone prediction

Being able to predict high concentrations of tropospheric ozone is important because of its negative impact on human health. In this paper eight regressor-selection methods are utilised in a case study for ozone prediction in the city of Nova Gorica, Slovenia. The comparison of the selected methods proved to be useful for building models that successfully predict the ozone concentrations for the treated case. Different regressors are selected for different models, with different methods based on the validation procedure’s cost functions. Namely, for the model to predict the maximum daily ozone concentration, ten regressors are selected; for the average concentration of ozone between 8.00 and 20.00 h, fifteen regressors are selected; and for the average daily concentration, ten regressors are selected. The result of the study is a regressor selection that is specific for a particular geographical location. Moreover, the study reveals that regressor selection, as well as the obtained models, differ depending on the kind of averaging interval of the ozone concentration.     

大型水轮机叶片的变形预测与控制研究

将数值模拟应用于大型水轮机叶片变形的预测与控制。综合考虑凝固、冷却、打箱、切除浇注系统和热处理影响建立数值模型,准确预测铸件在整个热加工工序中的变形。根据铸件结构特征和不同区域拘束度的不同,设置不同的反变形系数,对原始模型进行反变形处理。针对添加了反变形的模型,进行整个热加工过程的数值模拟计算,最终确定合理的铸件模样尺寸,生产出加工余量合适均匀的铸件。     

直缝焊管液压成形极限理论预测模型

直缝焊管广泛应用于汽车车身管状零件液压成形中,焊接区影响着焊管塑性变形规律,准确评价焊管缩颈或破裂现象是工程上倍受关注的问题。基于金相分析法和显微硬度测量法分析高频感应焊管的结构特征,并根据液压成形条件下高频感应焊管的变形特点,提出一种用于计算直缝焊管液压成形极限的理论方法。基于该方法,选用Swift硬化方程和Hill屈服准则推导出直缝焊管液压成形极限理论预测模型,在已知焊管(包含焊接区和基体区)材料性能参数条件下可获得直缝焊管液压成形极限图。运用此理论预测模型,计算出QSTE340高频感应焊管的液压成形极限图。成形极限的计算结果与试验对比表明,二者吻合较好,这证明所建立的直缝焊管液压成形极限的理论预测模型是正确的。     

PWM驱动电机系统传导干扰等效电路模型

采用脉宽调制技术（PWM）驱动的电机系统通过功率变换器对电能进行变换和控制,使得电机系统的性能指标得到较大提高。但是由于功率变换器中的电力电子器件工作在开关状态,具有较大电压和电流变化率的功率脉冲信号产生很强的电磁干扰,对电机系统自身和周围环境产生很大影响。本文针对PWM变频器产生的电磁兼容问题,首先分析了PWM驱动电...     

高潜水位矿区生态系统演变趋势研究:以淮南潘谢矿区为例

为了有效地应对高潜水位矿区煤炭资源开采后带来的生态环境问题,以淮南潘谢矿区为研究区域,应用遥感技术分析了耕地、水体分布以及景观格局,分析了生态系统结构的变化,得出了矿区生态系统结构总体变化规律以及不同生态系统相互转化规律。应用开采沉陷预测技术,基于矿区生态系统结构的变化规律,预测了生态系统演化趋势,得出的结论为:耕地面积逐年减少,矿区煤炭资源完全开采结束时,矿区的生态景观将由水域占绝对主导优势,矿区由陆地生态系统向水陆复合生态系统演变,土地利用模式将优化。