土壤源热泵在西安地区应用的数值模拟研究

针对西安地区的地质状况及气候特点,建立了管内流体、地埋管挟热器及周围土壤耦合传热模型,模拟了U型管内流体流动和传热、U型管与回填材料及土壤的传热.建立了连续模型和间歇模型,通过模拟分析得出结论:在西安地区应用土壤源热泵系统,热泵系统连续运行时间不能超过一定的时间(夏季为18h,冬季为7h),超出此时间后,由于换热器周围土壤中热量(或冷量)累积,使得换热环境恶化,换热器与周边土壤的换热量不断下降,导致热泵机组在不利工况下运行,长此以往,当U型管出口流体温度上升(下降到)到机组的保护温度时,压缩机将停止运行.与连续运行工况相比,在西安地区采用间歇运行工况时,地下换热器周围土壤可以得到一定程度的恢复,且间歇时间越长,恢复的程度越好.     

间隙性点源排放的一维移流离散

在给定的求解条件下 ,通过数学推导 ,给出了间隙性点源排放一维移流离散水质模型方程的解析解。分析了解的合理性 ,讨论了与可对比解析解的一致性 ,其结果是令人满意的     

基于Hilbert频移的EEMD谐波检测方法

针对经验模态分解(EMD)在谐波检测应用中产生模态混叠的问题,结合EMD分解的局限性和谐波检测实际情况进行分析。首先用集合经验模态分解(EEMD)消除EMD遇到间歇信号干扰出现的模态混叠问题,然后根据谐波信号间的密频问题,提出了基于Hilbert频移的EEMD谐波检测方法。该方法先对谐波信号进行EEMD分解,通过相关度判断相近信号是否发生混叠,若发生混叠,利用Hilbert频移方法使信号满足EEMD分解条件,从而将其分解为单频率分量信号。经仿真验证,该方法能够很好地克服谐波检测中的间歇信号干扰和信号间密频问题,保证了谐波信号有效分解和实用性。通过对实际整流信号的分析证明该方法具有很好的检测效果。     

智能接地配电系统

为了发挥中性点非有效接地配电网供电可靠性高的优越性,提出了一种通过在变电站配置智能接地装置并在馈线配置具有零序保护功能的配电终端和故障指示器,将配电网升级为智能接地配电系统的方法。发生单相接地时,智能接地装置可以迅速将故障相短暂金属性接地从而可靠熄灭电弧。若为永久性接地故障,随后控制中性点短暂投入中电阻以显著增大接地点上游的零序电流并基于配电终端和故障指示器处配置的零序电流保护可靠地实现单相接地选线、定位和隔离。论述了智能接地配电系统的组成和关键技术,重点论述了通过电阻分级开关应对选相错误并抑制开关操作引起的系统暂态过程的方法。结合实例论证了所建议的方法的可行性和有效性。     

间歇式新能源消纳评估常用方法分析

随着我国能源需求和环保意识的提升,以风电、光伏等间歇式新能源为代表的绿色能源装机容量持续上升。大规模间歇式新能源消纳受电源、网架及负荷等多方面因素影响,为提升新能源的开发利用率,须采用合适的消纳评估方法对消纳情况进行分析。解析法反映了间歇式新能源消纳问题的本质,文中首先阐述解析法的数学模型,并结合解析法的运用对该方法进行评价。然后,梳理时序生产模拟法及随机生产模拟法的原理及运用情况,这2种方法是以解析法原理为基础,分别针对建模时对间歇式新能源消纳的时序特性、随机特性考虑不足而提出的改进方法,虽然这2种方法的运用仍存在各自的局限性,但相较于解析法,两者的求解精度均有了较大提升。最后,结合现有技术对未来间歇式新能源消纳评估方法研究进行展望,为评估方法建模、运用及改进等提供参考。     

Probabilistic characterization of the effect of transient stochastic loads on the fatigue-crack nucleation time

The rainflow counting algorithm for material fatigue is both simple to implement and extraordinarily successful for predicting material failure times. However, it neglects memory effects and time-ordering dependence, and therefore runs into difficulties dealing with highly intermittent or transient stochastic loads with heavy tailed distributions. Such loads appear frequently in a wide range of applications in ocean and mechanical engineering, such as wind turbines and offshore structures. In this work we employ the Serebrinsky–Ortiz cohesive envelope model for material fatigue to characterize the effects of load intermittency on the fatigue-crack nucleation time. We first formulate efficient numerical integration schemes, which allow for the direct characterization of the fatigue life in terms of any given load time-series. Subsequently, we consider the case of stochastic intermittent loads with given statistical characteristics. To overcome the need for expensive Monte-Carlo simulations, we formulate the fatigue life as an up-crossing problem of the coherent envelope. Assuming statistical independence for the large intermittent spikes and using probabilistic arguments we derive closed expressions for the up-crossing properties of the coherent envelope and obtain analytical approximations for the probability mass function of the failure time. The analytical expressions are derived directly in terms of the probability density function of the load, as well as the coherent envelope. We examine the accuracy of the analytical approximations and compare the predicted failure time with the standard rainflow algorithm for various loads. Finally, we use the analytical expressions to examine the robustness of the derived probability distribution for the failure time with respect to the coherent envelope geometrical properties.     

Mathematical models of the transitions between endocrine therapy responsive and resistant states in breast cancer

Endocrine therapy, targeting the oestrogen receptor pathway, is the most common treatment for oestrogen receptor-positive breast cancers. Unfortunately, these tumours frequently develop resistance to endocrine therapies. Among the strategies to treat resistant tumours are sequential treatment (in which second-line drugs are used to gain additional responses) and intermittent treatment (in which a ‘drug holiday’ is imposed between treatments). To gain a more rigorous understanding of the mechanisms underlying these strategies, we present a mathematical model that captures the transitions among three different, experimentally observed, oestrogen-sensitivity phenotypes in breast cancer (sensitive, hypersensitive and independent). To provide a global view of the transitions between these phenotypes, we compute the potential landscape associated with the model. We show how this oestrogen response landscape can be reshaped by population selection, which is a crucial force in promoting acquired resistance. Techniques from statistical physics are used to create a population-level state-transition model from the cellular-level model. We then illustrate how this population-level model can be used to analyse and optimize sequential and intermittent oestrogen-deprivation protocols for breast cancer. The approach used in this study is general and can also be applied to investigate treatment strategies for other types of cancer.