考虑含水量的黄土高边坡稳定性预测模型研究

黄土高边坡土体的稳定主要受坡体含水量的控制,如果能提前根据含水量的变化预测边坡的安全系数和安全状态,就能及时采取安全措施,减少或避免由边坡失稳造成的损失。在搜集黄土高边坡工程典型实例资料的基础上,综合考虑影响边坡稳定的因素,根据边坡的几何、物理、力学参数构建训练样本和测试样本,基于LM算法的BP神经网络建立了黄土高边坡稳定性预测模型,并由资料拟合出黄土强度参数与含水量的关系式,由此提出了利用观测含水量预测关中地区高边坡稳定性的系统模型。最后以关中地区某一高边坡为例,简要介绍了该模型的使用。结果表明,模型的预测值和期望值吻合较好,具有较高的可靠性;当含水量超过13.4%时该边坡失稳,与实际情况吻合,说明该模型在关中地区具有较强的实用性。

米酸汤发酵工艺优化及贮存稳定性预测模型

该文研究米酸汤最优发酵工艺及米酸汤稳定性动力学和热力学模型,以取代传统米酸汤自然发酵工艺并延长产品的保质期。以发酵时间、菌种接种量、发酵温度以及初始pH值为自变量,进行单因素实验,再以总酸为响应值,进行响应面分析,得到米酸汤最佳发酵工艺条件。研究不同温度(25、35、45℃)对米酸汤品质的影响,通过分析离心沉淀率、粒径、色差值、黏度、pH值和感官评分等指标,建立动力学及热力学模型。优化后工艺条件参数为发酵时间104 h、接种量为5%、发酵温度为36℃、初始pH 6.6,在此条件下,R 2=0.97,总酸(9.48±0.08)g/L,与模型预测值9.43 g/L基本一致。在贮存期间离心沉淀率、色差值与感官评分的相关性最高,R 2均>0.98。该文建立离心沉淀率、色差值与感官评分、温度之间的动力学及热力学模型,计算出离心沉淀率和色差值预测模型误差CV平均值分别为13.46%、6.07%,均<15%。响应面法优化米酸汤配方切实可行,能缩短发酵周期,提高产品质量,对于米酸汤生产具有良好的实践参考价值。该模型可以用于米酸汤贮存稳定性预测,为米酸汤提供一种贮存稳定性快速预测方法。

基于贝叶斯的滑坡稳定性预测对比分析研究

滑坡稳定性的分析是一个复杂的系统工程,影响滑坡稳定性的因素较多。首先,基于相关系数理论描述滑坡稳定性影响因素对滑坡稳定状态影响的大小。再根据关联度大小筛选出影响滑坡稳定性的主要影响因素。其次,引入贝叶斯理论,滑坡稳定性的主要影响因素和滑坡的稳定状态建立了基于贝叶斯的滑坡稳定性预测模型。最后,在贝叶斯理论分析和MATLAB软件的基础上,以竹溪县197组滑坡数据中的100组滑坡数据作为训练样本,其余97组滑坡数据作为测试样本,代入模型进行判别。研究结果表明:其训练集稳定性预测的正确率为80%,测试集稳定性预测的正确率为80.41%。故基于贝叶斯的滑坡稳定性预测模型对于滑坡稳定性分析具有一定参考价值。同时,相关的方法也可以为其他工程研究提供参考。

一类带有输入和状态约束的非线性稳定性约束模型预测控制的研究

在稳定性约束模型预测控制中，稳定性约束可从一级传递到另一级来限制用可控结构形式表示的系统状态向量大小。本文探讨了把线性模型预测控制扩展到非线性稳定性约束模型预测的控制（ＳＣＭＰＣ）；在扩展的情况下，模型预测控制的最优化可直接用于非线性系统模型的控制，同时提供了稳定性约束模型预测控制用于带有输入和状态约束的非线性系统控制算法并将该算法用于仿真。仿真结果表明该算法是有效的。

Box-Behnken法对马奶啤酒发酵条件的优化及稳定性预测

以新鲜马乳、大麦芽及啤酒花为原料,采用二次生物发酵法制得马奶啤酒,在单因素基础上,选择接种比例(乳酸菌:酵母菌)、发酵温度、发酵时间及蔗糖添加量进行Box-Behnken响应面设计与分析,并通过热力学参数预测稳定性。结果表明,最佳发酵条件分别为接菌比例(乳酸菌/酵母菌)1:2、发酵温度32℃、发酵时间78 h、蔗糖添加量9%,发酵得到马奶啤酒酒精度可达到4.7%(vol)。稳定性预测模型与实测值误差小。

Grey relationship analysis and grey forecasting modeling on thermal stability of synthetic single diamond

Through analyzing 7 Ib-type samples of synthetic single diamonds by their DTA and TG in air, we ascertained the extrapolated onset temperature on the curves of DTA as the characteristic temperature of their thermal stabilities. Based on the grey system theory, we analyzed 4 factors influential in the thermal stability by the grey relationship analysis, a quantitative method, and derived the grey relationship sequence, that is, the rank of the influence extent of 4 factors on the thermal stability. Furthermore, we established the grey forecasting model, namely GM(1,5), for predicting the thermal stability of single diamonds with their intrinsic properties, which was then examined by a deviation-probability examination. The results illustrate that it is reasonable to take the Extrapolated Onset Temperature in DTA as the characteristic temperature for thermal stability (TS) of Ib-type synthetic single diamonds. The nitrogen content and grain shape regularity of diamonds are dominating factors. Likewise, grain size and compressive strength are minor factors. In addition, GM(1,5) can be used to predict the thermal stability of Ib-type synthetic single diamonds available. The precision rank of GM(1,5) is ‘GOOD’.

TIME SERIES FORECASTING WITH MULTIPLE CANDIDATE MODELS:SELECTING OR COMBINING?

Various mathematical models have been commonly used in time series analysis and forecasting. In these processes, academic researchers and business practitioners often come up against two important problems. One is whether to select an appropriate modeling approach for prediction purposes or to combine these different individual approaches into a single forecast for the different/dissimilar modeling approaches. Another is whether to select the best candidate model for forecasting or to mix the various candidate models with different parameters into a new forecast for the same/similar modeling approaches. In this study, we propose a set of computational procedures to solve the above two issues via two judgmental criteria. Meanwhile, in view of the problems presented in the literature, a novel modeling technique is also proposed to overcome the drawbacks of existing combined forecasting methods. To verify the efficiency and reliability of the proposed procedure and modeling technique, the simulations and real data examples are conducted in this study.The results obtained reveal that the proposed procedure and modeling technique can be used as a feasible solution for time series forecasting with multiple candidate models.

Numerical predictions and stability analysis of cavitating draft tube vortices at high head in a model Francis turbine

Draft tube vortex is one of the main causes of hydraulic instability in hydraulic reaction turbines,in particular Francis turbines.A method of cavitation calculations was proposed to predict the pressure fluctuations induced by draft tube vortices in a model Francis turbine,by solving RANS equations with RNG k-turbulence model and ZGB cavitation model,with modified turbulence viscosity.Three cases with different flow rates at high head were studied.In the study case of part load,two modes of revolutions with the same rotating direction,revolution around the axis of the draft tube cone,and revolution around the core of the vortex rope,can be recognized.The elliptical shaped vortex rope causes anisotropic characteristics of pressure fluctuations around the centerline of the draft tube cone.By analyzing the phase angles of the pressure fluctuations,the role of the vortex rope as an exciter in the oscillating case can be recognized.An analysis of Batchelor instability,i.e.instability in q-vortex like flow structure,has been carried out on the draft tube vortices in these three cases.It can be concluded that the trajectory for study case with part load lies in the region of absolute instability(AI),and it lies in the region of convective instability(CI)for study case with design flow rate.Trajectory for study case with over load lies in the AI region at the inlet of the draft tube,and enters CI region near the end of the elbow.