透析纸阻菌性影响因素分析

通过正交分析,对比了浆料配比、打浆度、施胶量及油压压力对透析纸阻菌性能的影响差异。研究结果表明:所研究的4种因素对阻菌性影响各异,其中施胶量的影响最为显著,打浆度居次,而浆料配比与油压压力的影响最小,为实际生产中透析纸阻菌性控制因素及操作顺序提供了依据。     

边坡软式透水管在不同淤塞度下的排水效应数值模拟

通过结合实际室内边坡试验成果,借助ＰＬＡＸＩＳ３Ｄ有限元软件建立边坡三维渗流模型,对软式透水管在不同淤塞程度的工况下的排水效应及其对边坡渗流场的影响展开研究。研究结果表明:边坡位移量从坡体内部逐渐向坡面发展演化,在透水管上方的坡面处位移变形量最大;渗流量随着软式透水管累积透水面积的增长而逐渐增加,透水管出水口处的流量最大,随透水管淤塞程度的增加,透水管的导水能力逐渐降低,即边坡坡体与透水管的总渗流量逐渐减少;透水管在较小程度的淤塞情况下仍可发挥部分排水功能,边坡渗流场的地下水位与孔隙水压力值有效降低,不会使坡体产生较大变形量。     

低压下加肥对迷宫滴头流量及灌水均匀度的影响--测试文章

针对滴灌中侧翼迷宫式滴灌带的流量、灌水均匀度、肥料堵塞等问题,对4种不同规格的侧翼迷宫滴灌带进行试验,测定在清水和加肥灌水时滴头的流量及灌水均匀度。结果表明：加肥灌水时滴头流量比清水时减少,灌水均匀系数也较清水减小,4种不同结构滴头的灌水均匀度系数减小了5%～9.9%;随着灌水间隔增长,灌水次数增加,由于溶解在水中化学肥料结晶析出造成的堵塞程度逐渐增加;迷宫流道结构不同滴头的化学堵塞程度也不同。     

1036MW超超临界机组海水脱硫系统取消旁路烟气挡板的实施经验

随着国家节能减排政策的全面落实,要求火电企业逐步拆除已建脱硫设施的旁路烟道.介绍了海水脱硫技术在华能海门电厂超超临界燃煤机组中的成功应用经验,并针对该系统取消旁路烟气挡板存在的实际问题进行分析,认为此项改造的核心问题是控制吸收塔进口烟气温度和防止烟气换热/加热器(gas-gas heater,GGH)、吸收塔发生堵塞,只有实现了这些目标才能确保脱硫系统各项排放指标合格和系统安全运行.在此基础上提出了一系列预控措施和优化技术.     

有机氟硼酸在王家岗敏感性地层的应用

根据3口井岩心矿物组成及碳酸盐含量数据,认为王家岗沙四段砂岩储层土酸和低伤害酸酸化效果差的原因,是储层碳酸盐、泥质含量高,酸岩反应速度快,反应产物产生一次及二次沉淀,乏酸液反排困难,在这类地层可采用有机氟硼酸体系。根据3口井储层岩心溶蚀率及酸化淤渣率数据,得到了各适用于某一口井的3个混合酸:4%/4%、6%/4%、4%/12%的醋酸/氟硼酸。筛选了酸液缓蚀剂KX1 7,N80钢片在加入1%KX1 7和5%NH4Cl的醋酸/氟硼酸液中80℃腐蚀速率为0.45～2.5g/m2·h。注入由3个混合酸配成的酸液使对应岩心的渗透率分别增大25%、76%、52%。在3口油井用该有机氟硼酸液酸化解堵,均获得了成功。较详细地介绍了在一口新投产不出油的井用醋酸/氟硼酸酸化液酸化,用液态CO2助排的施工情况和施工后产油情况。图3表5参3。     

Data-driven modelling of turbine wake interactions and flow resistance in large wind farms

Turbine wake and local blockage effects are known to alter wind farm power production in two different ways: (1) by changing the wind speed locally in front of each turbine and (2) by changing the overall flow resistance in the farm and thus the so-called farm blockage effect. To better predict these effects with low computational costs, we develop data-driven emulators of the ‘local’ or ‘internal’ turbine thrust coefficient $C_T^{\ast }$ as a function of turbine layout. We train the model using a multi-fidelity Gaussian process (GP) regression with a combination of low (engineering wake model) and high-fidelity (large eddy simulations) simulations of farms with different layouts and wind directions. A large set of low-fidelity data speeds up the learning process and the high-fidelity data ensures a high accuracy. The trained multi-fidelity GP model is shown to give more accurate predictions of $C_T^{\ast }$ compared to a standard (single-fidelity) GP regression applied only to a limited set of high-fidelity data. We also use the multi-fidelity GP model of $C_T^{\ast }$ with the two-scale momentum theory (Nishino & Dunstan 2020, J. Fluid Mech. 894, A2) to demonstrate that the model can be used to give fast and accurate predictions of large wind farm performance under various mesoscale atmospheric conditions. This new approach could be beneficial for improving annual energy production (AEP) calculations and farm optimization in the future.