横轴流脱粒分离装置的数学模型的建立与试验

横轴流脱粒分离装置滚筒长度限制了其脱粒分离能力,仅被应用于中小型联合收割机。为研究横轴流脱粒分离装置脱粒滚筒转速、喂入量、脱粒间隙等因素对脱粒分离性能的影响,优化装置结构,利用概率学理论建立了横轴流脱粒分离装置的未脱净率和夹带损失率数学模型。对模型正确性验证试验表明,模型对未脱净率的预测相对误差为8.23%,对夹带损失率的预测相对误差为2.90%。仿真分析和试验表明,该模型可反映籽粒轴向分布和脱粒滚筒转速、喂入量、脱粒间隙等参数对脱粒分离性能的影响。     

Trends in NE Atlantic landings (southern Portugal): identifying the relative importance of fisheries and environmental variables

Time series of commercial landings from the Algarve (southern Portugal) from 1982 to 1999 were analyzed using min/max autocorrelation factor analysis (MAFA) and dynamic factor analysis (DFA). These techniques were used to identify trends and explore the relationships between the response variables (annual landings of 12 species) and explanatory variables [sea surface temperature, rainfall, an upwelling index, Guadiana river (south‐east Portugal) flow, the North Atlantic oscillation, the number of licensed fishing vessels and the number of commercial fishermen]. Landings were more highly correlated with non‐lagged environmental variables and in particular with Guadiana river flow. Both techniques gave coherent results, with the most important trend being a steady decline over time. A DFA model with two explanatory variables (Guadiana river flow and number of fishermen) and three common trends (smoothing functions over time) gave good fits to 10 of the 12 species. Results of other models indicated that river flow is the more important explanatory variable in this model. Changes in the mean flow and discharge regime of the Guadiana river resulting from the construction of the Alqueva dam, completed in 2002, are therefore likely to have a significant and deleterious impact on Algarve fisheries landings.     

太湖平原水田区灌溉回归水分析

江苏太湖平原地区灌溉水源丰富,排水通畅,但由于年际水量分配不均,干旱仍时有发生。根据这一情况,对灌溉水的利用问题进行了研究。在大、小区水量平衡的基础上,提出了灌溉水的回归系数和回归水的重复利用系数,以及灌溉水的重复利用系数。为本区水利规划、用水管理和供水规划提供了可靠的依据。     

回转破碎气流干燥设备试验研究

介绍了回转破碎气流干燥设备的结构特点及其性能试验。该设备将滚筒破碎干燥机与气流干燥机串联组合，其组合衔接处采用封头板带过流孔的独特结构形式，把整个干燥过程分为三个阶段，集滚筒破碎干燥机、闪急干燥机和气流干燥机的优点于一体，提高了烘干效率和产品质量。     

剑麻种质资源表型多样性分析与倍性鉴定

利用常规方法结合流式细胞仪对25份剑麻种质资源叶色、叶缘刺有无、叶缘刺类型、叶顶刺类型、叶长、叶宽、叶长/宽比、叶基厚度、顶刺长、顶刺基部宽等10个农艺性状以及倍性进行了分析和鉴定,结果表明:同一种质资源内叶长、叶宽、叶长/宽比、叶基厚度、顶刺基部宽和顶刺长等变异系数分别在0.9%~13.33%、1.69%~9.13%、1.77%~7.61%、3.99%~11.62%、7.82%~14.31%、9.08%~14.84%之间,不同种质资源间变异系数分别为21.60%、13.95%、15.9%、21.48%、36.59%和30.77%。同一种质资源内叶长、叶宽、叶长/宽比、叶基厚度、顶刺基部宽和顶刺长差异不明显,不同种质资源间存在极显著差异(α=0.05)。基于10个表型性状将25份剑麻种质资源聚为3类,第一类主要包括番麻、广西76416、南亚1号、东368、蓝剑麻、普通剑麻、粤西75等7份种质;第二类主要包括桂辐4号、H.11648、东74、东27、东26和东16等6份种质;第三类主要包括东18、东2、肯3等12份种质。多变量的主成分分析显示4个主成分的特征值总和为5.84,累计贡献率达97.29%。流式细胞仪分析表明,25份剑麻种质资源中,H.11648、东2等14份种质为二倍体,东16和广西76416两份种质为三倍体,粤西75、粤西114等5份种质为四倍体,肯3和普通剑麻2份种质为五倍体,东74为六倍体,番麻为五倍体和六倍体的混倍体。以上结果为剑麻种质资源的鉴定评价提供理论依据。     

不同湍流模型在轴流泵性能预测中的应用

为了评价不同湍流模型在轴流泵性能预测中的精度,该文以南水北调工程轴流泵模型作为研究对象,分别选取了3种湍流模型标准k-ε湍流模型(standard k-ε)、重正化群k-ε湍流模型(renormalization group k-ε,RNG)和雷诺应力模型(reynolds stress model,RSM),基于SIMPLE算法(semi-implicit method for pressure-linked equations)和结构化网格,进行了轴流泵性能预测和全流场数值模拟,并以水利部天津同台测试的试验结果作为基准对预测扬程和效率进行了误差分析。研究结果表明,网格密度对模拟结果具有较大影响,较疏的网格导致性能预测精度降低,在大流量和小流量工况下预测的扬程和效率误差将达到3%以上;在最优工况下,Standard k-ε、RNG k-ε和RSM湍流模型的扬程预测误差分别为0.97%、1.12%和1.24%,效率预测误差分别为2.93%、2.49%和2.97%,可满足工程应用要求;但在非设计工况下,由于二次回流、空化等复杂流动的存在,内部流场复杂,3种湍流模型的扬程最大预测误差范围为9.40%～14.30%,效率最大预测误差范围为4.48%～8.30%。该结论将为轴流泵性能预测的可靠性提供依据。     

福建省南平5-17滑坡泥石流及防治对策

通过对福建省南平市延平区2002年5—17滑坡泥石流灾害的详细调查，综合分析了该滑坡泥石流形成的因素，阐述了灾害形成的原因。这次滑坡泥石流灾害是多因素综合作用的结果，但各因素所发挥作用的大小不一。主要因素是存在有利于滑坡形成的地质地貌条件，有长时间的暴雨激发，又有不合理的人为活动。针对灾害形成的原因提出了防治对策，以便对以后潜在的灾害进行调查与防治。     

红松林冠对降水的截留特征

通过野外测定与统计分析,系统研究了红松(Pinus koraiensis)林冠对降水的截留特征。在观测期间(2003年5-9月),红松林分林冠截留量为103.48mm,占同期降水量的26.19%;降水按以下比例被再次分配:穿透降水68.16%,林冠截留26.19%,树干径流5.65%。利用Horton改进模型进行模拟分析,结果表明:红松林分表征林冠吸附容量的参数Ic*m=2.387mm,大于该地区针叶树种樟子松和落叶松的吸附容量;表征区域降雨蒸发能力的参数α=13.9%。2个参数较好的解释了红松林冠截留吸附、湿润与蒸发机理;红松林冠截留率随降水的变化曲线可分为快变期(截留率>30%,降水量<15mm)、渐变期(截留率30%~18.67%,降水15~50mm)和稳定期(截留率<18.67%,降水>50mm)3个阶段,红松林冠稳定截留率为18.67%。红松林冠截留构成中吸附截留量较大。     

天津地区表流人工湿地冬季运行工艺研究

[目的]研究天津地区表流人工湿地冬季运行工艺。[方法]针对天津的气候特点,以冰下运行的方式,开展天津地区表流湿地冬季运行试验,研究自由表面流人工湿地北方冬季运行工艺参数。[结果]虽然水质净化效果差于其他温暖季节(平均降低约20%),但是在天津地区表流湿地采取冰下运行的方式是可行的。北方表流人工湿地冬季冰下运行时应将出水口地势降低,防止出水口被完全冰冻,运行水深不宜50～60 cm,可以在回流的基础上适当加大水力负荷(水力停留时间以4 d为宜),以维持水土界面不会冻结,并尽可能加大取水深度,以提高系统水温。[结论]该研究可为表流人工湿地在我国北方的推广应用提供借鉴。     

Effect of Graded Levels of Fiber from Alfalfa Meal on Nutrient Digestibility and Flow of Fattening Pigs

The fiber level and composition have an important effect on nutrient digestibility of swine diets. Little information is known about the effects of fiber level and composition from alfalfa meal on nutrient digestibility of fattening pigs fed a corn-soybean meal-based diet. The objective of this experiment was to determine the effects of alfalfa fiber on the growth performance, intestinal nutrient flow and apparent total tract digestibility（ATTD） of nutrients in fattening pigs. 24 barrows（Duroc×（Large White×Landrace）, body weight=（60.6±0.7） kg） were randomly allotted to 4 treatments with 6 replicates of 1 pig per replicate. The pigs were provided a control diet or a diet containing 5, 10 or 20% of alfalfa meal during a 14-d experiment period. Average daily gain（ADG） and the ATTD of dry matter（DM）, organic matter（OM）, crude protein（CP）, neutral detergent fiber（NDF）, acid detergent fiber（ADF） and gross energy（GE） reduced linearly as the level of alfalfa meal in the diet increased（P〈0.01）. The total tract flow of DM, OM, CP, NDF, ADF, and GE increased with the increase in dietary alfalfa（linear, P〈0.05）. Growth performance and nutrient digestion were not affected by inclusion of 5% alfalfa meal in the diet（P〉0.05）. A multiple linear regression analysis, taking into account both soluble and insoluble fiber intake, explained approximately 70% of the variation in the ATTD of DM, OM, NDF, and GE（P〈0.01）. In conclusion, alfalfa meal should be limited to less than 5% of the diet in fattening pigs to maximize growth performance and nutrient digestion. Soluble and insoluble fiber from alfalfa meal has the differential roles in nutrient digestion, which may help explain the main variation observed in nutrient digestibility. These findings suggest that knowledge of specific fiber components is necessary to accurately predict the effects of dietary fiber on nutrient digestibility.