抗动能多功能复合材料结构模型试验研究

根据超高速碰撞破坏原理，对抗动能多功能复合材料的结构模型进行了试验研究，并设计了数种结构模型。实验结果表明，所提出的陶瓷基复合材料迎击面加固防护层，多孔复合基体缓冲层和Kevlar/织物增强环氧树脂结构层等试验模型具有良好的抗超高速撞击功能。     

磁流变液的汽车碰撞缓冲吸能装置设计

为了解决目前汽车保险杠刚度不可控、对不同碰撞环境适应性差的问题,针对磁流变液流变特性可控且吸能量大的特点,设计出阻尼力可控的保险杠缓冲吸能装置。以最大阻尼力和动态范围为优化目标,采用Matlab遗传算法对结构参数进行优化。分别建立装有传统吸能式保险杠和装有磁流变液缓冲吸能装置的整车碰撞模型,进行碰撞仿真实验。仿真结果表明:装有磁流变液缓冲吸能装置的汽车整车变形和最大碰撞力明显减小,可以有效减小对人员的伤害,提高汽车的被动安全性。     

基于有限元分析的笔记本纸浆模制品包装改进设计

目的在跌落工况下验证笔记本纸浆模制品对产品的保护性,并进行改进设计。方法使用Creo对笔记本电脑及缓冲包装结构进行初步设计,根据实际的物理跌落试验工况建立跌落仿真模型,通过ABAQUS/Explicit进行仿真分析,得到产品跌落加速度曲线和产品应力应变云图。根据仿真结果进行缓冲包装设计改进,对改进后的方案再次进行仿真分析并进行试验验证。结果初步设计方案的正面跌落加速度达到160.1g,超过规定值120g,且键盘面板塑性应变达到4.95%,存在较大变形风险。纸浆模制品缓冲结构改进后,跌落时正面、侧面和底面的跌落加速度分别为118.5g、98.2g、101.2g,均处于规定值范围内。键盘面板塑性应变降低至0.14%,符合要求。此外,仿真数据与实测结果基本吻合,仿真过程能较好地反映包装制品与产品的跌落碰撞过程。结论通过有限元分析法进行跌落仿真分析,相较于传统的试验法,能快速、准确地找到包装方案的风险点,可为产品的缓冲包装结构改进设计奠定良好基础。     

落石冲击下桥墩刚柔叠层防护结构的缓冲耗能试验研究

山区工程结构的落石冲击灾害问题显著,耗能缓冲防护结构能有效减小冲击灾害。针对桥墩的冲击防护,设计了型钢-泡沫板、型钢-混凝土和泡沫板-混凝土三种耗能缓冲结构,通过对三种防护结构在落石冲击下的破坏模式及试验动力响应过程分析,揭示刚柔叠层冲击防护结构的耗能缓冲机理。试验结果表明：利用刚性外层可将冲击能量有效扩散至内部柔性缓冲层,充分发挥刚柔层的耗能及缓冲性能;混凝土泡沫板防护结构综合性能最优,对比无防护结构,其可使落石冲击钢筋混凝土试验板的冲击持续时间延长9～10倍,平均冲击力减至无防护结构的1/10以下,具有良好的耗能缓冲性能。     

新型汽车缓冲器碰撞特性的数值仿真

汽车碰撞缓冲器的研制开发是汽车安全性研究的一个重要组成部分.本文针对新型机械吸能式汽车缓冲装置,采用对称罚函数的接触算法,利用显式动力分析有限元软件LS-DYNA,计算模拟了汽车缓冲器在碰撞过程中的动力响应,从而考察缓冲器的碰撞吸能特性.与汽车碰撞试验方法相比,计算机模拟具有可重复性好、周期短、成本低的优点,该方法对实车碰撞试验具有实际指导意义和工程应用价值.     

汽车碰撞事故缓冲吸能装置设计

本文在系统地研究了国内外汽车碰撞缓冲吸能方法的基础上,通过在车辆纵梁和前保险杠之间安装一套可伸缩的缓冲吸能装置,以实现在碰撞前将设计安装在原吸能梁中的辅助吸能梁及保险杠的中段伸出车外并将其限位,使其参与碰撞吸能,达到增加吸能空间,延长碰撞时间历程的效果。该装置具有结构简单紧凑、成本低廉、可靠性强等优点,具有重要的研究意义和实用价值。     

航空发动机离合器试验台扭矩测控系统的实现

提出了一种由异步电动机实现扭矩加载和控制的新方法，不需扭矩传感器和专门的加载装置实现了扭矩的测量和控制。采用Fuzzy-PID控制算法代替常规PID算法，能较好地模拟离合器的磨合、接合和超越等性能试验。     

神经网络对结构地震反应的预测及试验研究

本文基于神经网络对非线性系统具有辨识和预测功能，并结合具有二阶收敛效应的Levenberg—Marquardt算法，采用一多层前馈网络对建筑结构的地震反应进行了预测。首先以一三层钢筋混凝土结构的振动台试验数据对网络结构进行批量训练，然后用未曾训练的地震波数据对结构进行地震反应预测，并与试验数据进行对比，分析结果表明：Lev—enberg—Marquardt算法能快速收敛，神经网络能准确地预测结构的地震反应。     

缓冲材料冲击试验机的数据采集和处理系统

缓冲材料的缓冲特性是缓冲防振包装设计的重要依据。材料的缓冲特性可用最大加速度—静应力曲线或缓冲系数—最大应力曲线表示。材料缓冲特性的试验方法有动态压缩试验法和静态压缩试验法，与静态压缩试验法相比，动态压缩试验法更接近包装件跌落的实际状况，所以测试结果...     

缓冲衬垫CAD系统的研究

传统的缓冲包装设计，考虑流通环境，根据实验或经验得出的产品脆值，确定缓冲衬垫的种类及结构尺寸，然后进行校核。通常要反复实验与比较，才能筛选出合理的设计结果。其设计在很大程度上依赖于设计人员的自身经验、破坏性试验所得的数据，这就造成产品和缓冲材料的浪费，并且设计过程需时较长，在一定程度上制约了缓冲包装设计的发展。利用基于VC平台开发的缓冲衬垫CAD系统进行缓冲包装设计，能使设计人员从繁杂的手工设计中解放出来，显著地缩短了产品的研发周期，避免了对产品的破坏性试验。对于产品的包装，特别是对于精密电子产品的包装，其经济效益是相当可观的。开发缓冲衬垫CAD系统进行计算机辅助设计，是缓冲包装发展的必然趋势。     

Improving the crashworthiness of reinforced wooden road safety barrier using simulations of pre-stressed bolt connections with failure

During the development process of a new type of steel reinforced wooden road safety barrier parametric computational simulations were used to simulate the experimental vehicle impact certification tests as prescribed by the standard EN 1317. First a detailed study of pre-stressed bolt connection behavior between the guardrail and the guardrail connector was performed using parametric computational simulations of which results were later used in a large scale vehicle impact simulations. A novel, simplified approach to the modeling of barrier wooden parts was introduced to achieve reasonable simulation times in parametric study of the barrier behavior under vehicle impact. The wooden parts of the road safety barrier were modeled indirectly through a modified contact definition. The developed safety barrier design was later successfully experimentally certified in a full scale crash test according to the standard EN 1317. Experimental results were in a good agreement with the results of the full scale crash test simulations, which validates the proposed computational safety barrier model and thus justifies the use of the simplified modeling approach of the wooden safety barrier parts.     

Multi-regression modeling for springback effect on automotive body in white stamped parts

The use of high strength steel (HSS) materials in automotive body in white (BIW) stamped parts has increased the occurrence of springback after the forming process. Although HSS exhibits superior strength, weight reduction, and crash energy, it strongly influences springback impact on the sustainable development of BIW stamped parts. In this study, an empirical springback prediction model was synthesized based on the contemporary data sets of springback-prone components of automotive BIW stamped parts. Two different BIW stamped parts from an actual industrial stamping production line were selected as pilot parts for this study. A statistical multi-regression (MR) analysis was used to model the springback prediction effect by examining the sensitivity of springback input parameters on existing die geometry. The outputs represent the total springback values of the stamped parts. A total of 240 data from samples of selected stamped parts were tabulated to synthesize the springback prediction model. The results show that the MR models for the two parts were linear with the springback estimated errors between the measured and predicted values between 0.5° and 3°, which is acceptable from an industrial viewpoint. The proposed MR models are capable of predicting the springback effect with minimal error by incorporating all possible variations that are inherent in the shop floor process.     

碳化硼超细微粉团聚及解决方法

对碳化硼超细微粉在干燥时的硬团聚现象进行了分析,并分别采用喷雾干燥和气流破碎两种方法对物料进行处理.喷雾干燥能避免硬团聚现象的发生,但其能耗要比厢式干燥器高,较细的颗粒需要回收.利用气流磨时干燥后的碳化硼超细粉进行解聚,有效地解决了颗粒在干燥过程中发生硬团聚的问题,物料收率高,分级轮的转速很低,几乎没有磨损.     

Prioritizing Highway Safety Manual’s crash prediction variables using boosted regression trees

The Highway Safety Manual (HSM) recommends using the empirical Bayes (EB) method with locally derived calibration factors to predict an agency’s safety performance. However, the data needs for deriving these local calibration factors are significant, requiring very detailed roadway characteristics information. Many of the data variables identified in the HSM are currently unavailable in the states’ databases. Moreover, the process of collecting and maintaining all the HSM data variables is cost-prohibitive. Prioritization of the variables based on their impact on crash predictions would, therefore, help to identify influential variables for which data could be collected and maintained for continued updates. This study aims to determine the impact of each independent variable identified in the HSM on crash predictions. A relatively recent data mining approach called boosted regression trees (BRT) is used to investigate the association between the variables and crash predictions. The BRT method can effectively handle different types of predictor variables, identify very complex and non-linear association among variables, and compute variable importance. Five years of crash data from 2008 to 2012 on two urban and suburban facility types, two-lane undivided arterials and four-lane divided arterials, were analyzed for estimating the influence of variables on crash predictions. Variables were found to exhibit non-linear and sometimes complex relationship to predicted crash counts. In addition, only a few variables were found to explain most of the variation in the crash data.     

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states-perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of "excess" zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to "excess" zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed-and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros.     

Computational strategy for the crash design analysis using an uncertain computational mechanical model

The framework of this paper is the robust crash analysis of a motor vehicle. The crash analysis is carried out with an uncertain computational model for which uncertainties are taken into account with the parametric probabilistic approach and for which the stochastic solver is the Monte Carlo method. During the design process, different configurations of the motor vehicle are analyzed. Usual interpolation methods cannot be used to predict if the current configuration is similar or not to one of the previous configurations already analyzed and for which a complete stochastic computation has been carried out. In this paper, we propose a new indicator that allows to decide if the current configuration is similar to one of the previous analyzed configurations while the Monte Carlo simulation is not finished and therefore, to stop the Monte Carlo simulation before the end of computation.     

多用途汽车正面碰撞特性的数值模拟

为保证驾驶员和乘员的安全性,综合运用多种有限元分析软件,建立多用途汽车的有限元正面碰撞模型。通过对正面碰撞模型进行数值模拟、分析碰撞仿真结果,得出整车动能、内能和沙漏能在碰撞瞬间的转换关系,获得碰撞过程中车身前围关键零件的能量吸收程度和变形形式,由此提出提高整车正面抗撞性的方法和措施。仿真结果表明：该方法可用于乘用车的正面碰撞等仿真分析,能达到降低研发周期、提高分析精度的目的。     

Forgeability test of extruded Mg–Sn–Al–Zn alloys under warm forming conditions

Magnesium (Mg) alloys have been thoroughly researched to replace steel or aluminum parts in automotives for reducing weight without sacrificing their strength. The widespread use of Mg alloys has been limited by its insufficient formability, which results from a lack of active slip systems at room temperature. It leads to a hot forming process for Mg alloys to enhance the formability and plastic workability. In addition, forged or formed parts of Mg alloys should have the reliable initial yield and ultimate tensile strength after hot working processes since its material properties should be compatible with other parts thereby guaranteeing structural safety against external load and crash. In this research, an optimal warm forming condition for applying extruded Mg–Sn–Al–Zn (TAZ) Mg alloys into automotive parts is proposed based on T-shape forging tests and the feasibility of forged parts is evaluated by measuring the initial yield strength and investigating the grain size in orientation imaging microscopy (OIM) maps.     

Analysis of driver and passenger crash injury severity using partial proportional odds models

The question of whether crash injury severity should be modeled using an ordinal response model or a non-ordered (multinomial) response model is persistent in traffic safety engineering. This paper proposes the use of the partial proportional odds (PPO) model as a statistical modeling technique that both bridges the gap between ordered and non-ordered response modeling, and avoids violating the key assumptions in the behavior of crash severity inherent in these two alternatives. The partial proportional odds model is a type of logistic regression that allows certain individual predictor variables to ignore the proportional odds assumption which normally forces predictor variables to affect each level of the response variable with the same magnitude, while other predictor variables retain this proportional odds assumption. This research looks at the effectiveness of this PPO technique in predicting vehicular crash severities on Connecticut state roads using data from 1995 to 2009. The PPO model is compared to ordinal and multinomial response models on the basis of adequacy of model fit, significance of covariates, and out-of-sample prediction accuracy. The results of this study show that the PPO model has adequate fit and performs best overall in terms of covariate significance and holdout prediction accuracy. Combined with the ability to accurately represent the theoretical process of crash injury severity prediction, this makes the PPO technique a favorable approach for crash injury severity modeling by adequately modeling and predicting the ordinal nature of the crash severity process and addressing the non-proportional contributions of some covariates.