货车货厢振动环境测试与数据处理

为获取某车载物资随机振动环境试验条件,实测了两种货车货厢在不同路面激励下的振动响应.以该实测数据的处理和归纳为例,介绍了随机振动环境测试数据的处理和归纳方法.     

Simulation of Ekman Boundary Layers by Large Eddy Model with Dynamic Mixed Subfilter Closure

Theoretical analysis of boundary layer turbulence has suggested a feasibility of sufficiently accurate turbulence resolving simulations at relatively coarse meshes. However, large eddy simulation (LES) codes, which employ traditional eddy-viscosity turbulence closures, fail to provide adequate turbulence statistics at coarse meshes especially within a surface layer. Manual tuning of parameters in these turbulence closures may correct low order turbulence statistics but severely harms spectra of turbulence kinetic energy (TKE). For more than decade, engineering LES codes successfully employ dynamic turbulence closures. A dynamic Smagorinsky turbulence closure (DSM) has been already tried in environmental LES. The DSM is able to provide adequate turbulence statistics at coarse meshes but it is not completely consistent with the LES equations. This paper investigates applicability of an advanced dynamic mixed turbulence closure (DMM) to simulations of Ekman boundary layers of high Reynolds number flows. The DMM differs from the DSM by explicit calculation of the Leonard term in the turbulence stress tensor. The Horizontal Array Turbulence Study (HATS) field program has revealed that the Leonard term is indeed an important component of the real turbulence stress tensor. This paper presents validation of a new LES code LESNIC. The study shows that the LES code with the DMM provides rather accurate low order turbulence statistics and the TKE spectra at very coarse meshes. These coarse LES maintain more energetic small scale fluctuations of velocity especially within the surface layer. This is critically important for success of simulations. Accurate representation of higher order turbulence statistics, however, requires essentially better LES resolution. The study also shows that LES of the Ekman boundary layer cannot be directly compared with conventionally neutral atmospheric boundary layers. The depth of the boundary layer is an important scaling parameter for turbulence statistics.     

Renormalized numerical simulation of flow over planar and non-planar fractal trees

We review the fundamentals of a new numerical modeling technique called Renormalized Numerical Simulation (RNS). The goal of RNS is to model the drag force produced by high Reynolds-number turbulent flow over objects that display scale-invariant properties, objects such as tree-like fractals. The hallmark of RNS in this application is that the drag of the unresolved tree branches is modeled using drag coefficients measured from the resolved branches and unresolved branches (as modeled in previous iterations of the procedure). In the present paper, RNS is used to study the effects of branch orientation on the drag force generated by highly idealized trees in which trunk and branches have square cross-section, and the branches all lie in a plane perpendicular to the incoming flow. Then, the procedure is generalized to the more general case of non-planar branch arrangements. Results illustrate that RNS may enable numerical modeling of environmental flow processes associated with fractal geometries using affordable computational resolution.     

板料成形过程系统分析

此文从系统的角度就板料性能、板料毛坯的展开计算、模具设计与制造、摩擦与润滑、应力应变分析等诸多方面对板料成形过程作了一般分析.可知板料成形过程是一复杂的,需要考虑系统中各个因素之间的相互关系和影响才能得到合理的板料成形件.并从有限元法的角度,建立了板料成形过程模型.     

Safety effectiveness and crash cost benefit of red light cameras in Missouri

Objective: Red light cameras (RLCs) have generated heated discussions over issues of safety effectiveness, revenue generation, and procedural due process. This study focuses on the safety evaluation of RLCs in Missouri, including the economic valuation of safety benefits. The publication of the national Highway Safety Manual (HSM; American Association of State Highway and Transportation Officials) in 2010 produced statistical safety models for intersections and spurred the calibration of these models to local conditions.

Methods: This study adds to existing knowledge by applying the latest statistical methodology presented in the HSM and more current data. Driver behavior constantly changes due in part to driving conditions and the use of technology. The safety and economic benefit evaluation was performed using the empirical Bayes method, which accounts for regression to the mean bias. For the economic benefit evaluation, the KABCO crash severity scale and crash cost estimates were used. A total of 24 4-leg urban intersections were randomly selected from a master list of RLCs in Missouri from 2006 to 2011. Additionally, 35 comparable nontreated intersections were selected for the analysis.

Results and Conclusions: The implementation of RLCs reduced overall angle crashes by 11.6%, whereas rear-end crashes increased by 16.5%. The net economic crash cost benefit of the implementation of RLCs was $35,269 per site per year in 2001 dollars (approximately $47,000 in 2015 dollars). Thus, RLCs produced a sizable net positive safety benefit that is consistent with previous statistical studies.     

Working conditions and sleepiness while driving among truck drivers

Objective: The objective of this study is to identify the role of working conditions as predictors of sleepiness while driving among truck drivers.

Methods: This was a cross-sectional study carried out among truck drivers who transported grains to Paranaguá Port, Paraná, Brazil. The truck drivers were interviewed and completed a self-administered questionnaire to collect data on sociodemographic and behavioral variables, working conditions, consumption of illicit psychoactive substances, and sleep patterns. Drivers were considered to be sleepy while driving if they reported a medium or high probability of napping while driving at night, during the daytime, or while stopped in traffic. The statistical analysis used logistic regression models progressively adjusted for age, behavioral variables, sleep duration, and other working conditions.

Results: In total, 670 male drivers, with a mean age of 41.9 (±11.1) years, were enrolled. The prevalence of sleepiness while driving was 31.5%. After model adjustments, the following working conditions were associated with sleepiness while driving: Distance from the last shipment of more than 1,000?km (odds ratio [OR]?=?1.54; 95% confidence interval [CI], 1.07–2.23) and a formal labor contract with a productivity-based salary (OR = 2.65; 95% CI, 1.86–3.78). Consumption of illicit psychoactive substances (OR = 1.99; 95% CI, 1.14–3.47) was also associated with sleepiness while driving.

Conclusions: Distance traveled and a formal labor contract with productivity-based earnings were the working conditions associated with sleepiness while driving, regardless of other working or behavioral characteristics, age, consumption of illicit psychoactive substances, and sleep duration.     

Prediction of vehicle crashes by drivers' characteristics and past traffic violations in Korea using a zero-inflated negative binomial model

Aims: Traffic safety is a significant public health challenge, and vehicle crashes account for the majority of injuries. This study aims to identify whether drivers' characteristics and past traffic violations may predict vehicle crashes in Korea.

Methods: A total of 500,000 drivers were randomly selected from the 11.6 million driver records of the Ministry of Land, Transport and Maritime Affairs in Korea. Records of traffic crashes were obtained from the archives of the Korea Insurance Development Institute. After matching the past violation history for the period 2004–2005 with the number of crashes in year 2006, a total of 488,139 observations were used for the analysis. Zero-inflated negative binomial model was used to determine the incident risk ratio (IRR) of vehicle crashes by past violations of individual drivers. The included covariates were driver's age, gender, district of residence, vehicle choice, and driving experience.

Results: Drivers violating (1) a hit-and-run or drunk driving regulation at least once and (2) a signal, central line, or speed regulation more than once had a higher risk of a vehicle crash with respective IRRs of 1.06 and 1.15. Furthermore, female gender, a younger age, fewer years of driving experience, and middle-sized vehicles were all significantly associated with a higher likelihood of vehicle crashes.

Conclusions: Drivers' demographic characteristics and past traffic violations could predict vehicle crashes in Korea. Greater resources should be assigned to the provision of traffic safety education programs for the high-risk driver groups.     

Undercounting of large trucks in federal and state crash databases: Extent of problem and how to improve accuracy of truck classifications

Objective: Prior research suggested that single-unit trucks are undercounted when using vehicle body codes in the Fatality Analysis Reporting System (FARS). This study explored the extent of the misclassification and undercounting problem for crashes in FARS and state crash databases.

Methods: Truck misclassifications for fatal crashes were explored by comparing the Trucks Involved in Fatal Accidents (TIFA) database with FARS. TIFA used vehicle identification numbers (VINs) and survey information to classify large trucks. This study used VINs to improve the accuracy of large truck classifications in state crash databases from 5 states (Delaware, Maryland, Minnesota, Nebraska, and Utah).

Results: The vehicle body type codes resulted in a 19% undercount of single-unit trucks in FARS and a 23% undercount of single-unit trucks in state databases. Tractor-trailers were misclassified less often. Misclassifications occurred most frequently among single-unit trucks in the weight classes of 10,001–14,000 pounds.

Conclusions: The amount of misclassification of large trucks is large enough to potentially affect federal and state decisions on traffic safety. Using information from VINs results in more complete and accurate counts of large trucks involved in crashes. The National Transportation Safety Board recommended actions to improve federal and state crash data.