发动机水下排气噪声预报研究

由于涉及到两相流,所以发动机水下排气噪声的产生机理和辐射特性非常复杂。文章基于Mixture多相流模型、RNGκ-ε湍流模型和Ffowcs Williams-Hawking声学模型对发动机水下排气噪声进行了数值预报研究,并试验测量了相应工况下发动机水下排气噪声,对数值研究的结果进行了验证。结果表明,文中所提出的发动机水下排气噪声数值预报方法在工程上是可行的。     

火箭炮智能配电系统可靠性分析与设计

在分析某型火箭炮智能配电系统结构和工作原理的基础上,建立了某型火箭炮智能配电系统可靠性模型,进行配电系统可靠性预计,为配电系统设计方案选择提供基本的可靠性数据,通过＂预计-改进设计＂的循环,保证配电系统可靠性设计达到规定的要求。     

基于有效点导纳的机械设备激励特性的转换关系研究

针对机械设备安装到位后的振动预估问题,提出了一种基于有效点导纳的机械设备激励特性的转换方法。首先通过有效点导纳法将多点安装设备简化为多个单点安装设备,简化各个机脚之间的耦合作用;然后用自由速度作为设备的激励特性参数,建立不同安装基座的机械设备激励特性的转换关系;最后用弹性安装的电机进行了实验验证,将转换后基座振速的估算值与测量值进行对比分析。结果显示频率在20~1 000 Hz之间时,估算值总级的误差小于3 dB,表明该转换关系能够有效预估机械设备安装到位后的振动。     

QLY25B型轮胎式起重机行走减速箱（QLY25A-21C）的改造

日照港西港区件杂货码头QLY25B型轮胎式起重机经过数年使用,近年行走机构故障频发,尤其是行走减速箱部分齿轮磨损严重,必须进行改造。阐述了起重机行走减速箱工作原理,分析了故障原因,提出了改进措施。改造后的行走减速箱使用良好,行走机构故障率明显下降。     

Embedding risk attitude and decision weights in non-linear logit to accommodate time variability in the value of expected travel time savings

In recent years we have seen important extensions of logit models in behavioural research such as incorporation of preference and scale heterogeneity, attribute processing heuristics, and estimation of willingness to pay (WTP) in WTP space. With rare exception, however, a non-linear treatment of the parameter set to allow for behavioural reality, such as embedded risk attitude and perceptual conditioning of occurrence probabilities attached to specific attributes, is absent. This is especially relevant to the recent focus in travel behaviour research on identifying the willingness to pay for reduced travel time variability, which is the source of estimates of the value of trip reliability that has been shown to take on an increasingly important role in project appraisal. This paper incorporates, in a generalised non-linear (in parameters) logit model, alternative functional forms for perceptual conditioning (known as probability weighting) and risk attitude in the utility function to account for travel time variability, and then derives an empirical estimate of the willingness to pay for trip time variability-embedded travel time savings as an alternative to separate estimates of time savings and trip time reliability. We illustrate the richness of the approach using a stated choice data set for commuter choice between unlabelled attribute packages. Statistically significant risk attitude parameters and parameters underlying decision weights are estimated for multinomial logit and mixed multinomial logit models, along with values of expected travel time savings.     

A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction

This paper presents a Bayesian inference-based dynamic linear model (DLM) to predict online short-term travel time on a freeway stretch. The proposed method considers the predicted freeway travel time as the sum of the median of historical travel times, time-varying random variations in travel time, and a model evolution error, where the median is employed to recognize the primary travel time pattern while the variation captures unexpected supply (i.e. capacity) reduction and demand fluctuations. Bayesian forecasting is a learning process that revises sequentially the state of a priori knowledge of travel time based on newly available information. The prediction result is a posterior travel time distribution that can be employed to generate a single-value (typically but not necessarily the mean) travel time as well as a confidence interval representing the uncertainty of travel time prediction. To better track travel time fluctuations during non-recurrent congestion due to unforeseen events (e.g., incidents, accidents, or bad weather), the DLM is integrated into an adaptive control framework that can automatically learn and adjust the system evolution noise level. The experiment results based on the real loop detector data of an I-66 segment in Northern Virginia suggest that the proposed method is able to provide accurate and reliable travel time prediction under both recurrent and non-recurrent traffic conditions.     

Bus arrival time prediction at bus stop with multiple routes

Provision of accurate bus arrival information is vital to passengers for reducing their anxieties and waiting times at bus stop. This paper proposes models to predict bus arrival times at the same bus stop but with different routes. In the proposed models, bus running times of multiple routes are used for predicting the bus arrival time of each of these bus routes. Several methods, which include support vector machine (SVM), artificial neural network (ANN), k nearest neighbours algorithm (k-NN) and linear regression (LR), are adopted for the bus arrival time prediction. Observation surveys are conducted to collect bus running and arrival time data for validation of the proposed models. The results show that the proposed models are more accurate than the models based on the bus running times of single route. Moreover, it is found that the SVM model performs the best among the four proposed models for predicting the bus arrival times at bus stop with multiple routes.     

灰色-马尔科夫油料消耗预测模型

运用灰色-马尔科夫模型对某油库油料消耗进行预测,克服了灰色预测与马尔科夫预测各自的缺点和不足,提高了预测的精度,增强了预测结果的可靠性、科学性和实用性。     

耙吸挖泥船拖力预报研究

在单桨船自航性能预报二因次与三因次标准方法与规范的基础上,提出一种耙吸拖力自航试验及实船耙吸拖力预报方法。选取5条不同型号的双桨耙吸挖泥船进行模型试验,分别用二因次与三因次方法对耙吸拖力进行预报。根据预报结果对三因次法中的功率因子与转速因子进行回归分析,比较两种方法的最终预报结果,验证了所提方法的合理性、有效性。     

基于复合预测控制的并联有源电力滤波器研究

通过建立线性预测数学模型,分析了线性预测的不稳定性和差一拍控制对谐波补偿输出的影响,提出了将线性预测与记录一周期谐波电流采样点作为后续预测电流相复合的无差拍控制,消除了采样与计算延时对系统的影响。仿真结果表明,平均滤波器应用于ip-iq谐波电流检测算法中,谐波检测精度较高、响应速度较快;采用复合无差拍控制,对谐波抑制与无功补偿有较好的稳态精度和动态响应速度。在整体上提高了并联有源电力滤波器的谐波抑制与无功补偿性能。