一次枝晶间距模型及在单晶高温合金中的验证

本文评述了二元系统一次枝晶间距模型的发展及特点，并验证了在多元合金系统中的适用性，结果表明，在ＤＤ８单晶高温合金中，λ１－Ｇ关系符合二元系统模型，但λ１－Ｖ关系和二元系统模型有较大的偏离，λ１为一次枝晶间距，Ｇ为温度梯度，Ｖ为凝固速率，在本文凝固条件下，ＤＤ８合金的λ１－Ｇ，Ｖ关系式为：λ１＝１１４１．５Ｇ＾－０．５２Ｖ＾－０．１２，其中λ１单位为μｍ，Ｇ为Ｋ／ｍｍ，Ｖ为μｍ／ｓ。     

镍基高温合金超细定向柱晶的凝固特性

本文采用超高梯度快速定向凝固装置研究了镍基铸造高温合金 K405超细定向柱晶的凝固特性。试验结果表明,当温度梯度为660K/cm 和凝固速率为760μm/s时,可获得超细柱晶体,其一次、二次枝晶间距λ_1和λ_2分别小于25μm 和10μm;Cr,Al,Ti,W,Co 等元素的偏析比均趋近于1;γ′相分布均匀,(γ+γ′)共晶基本消除,MC 碳化物呈点状和小条状沿一次枝晶轴方向均匀分布于枝晶间。     

温度梯度对定向凝固Al-4.5%Cu合金一次枝晶间距的影响

使用5kg坩埚下拉式定向凝固设备,对Al-4.5%Cu合金进行定向凝固试验,研究Al-4.5%Cu合金在不同温度梯度下一次枝晶间距的变化规律。结果表明:在其他凝固参数不变时,随着温度梯度的增大,一次枝晶逐渐变小,细密,挺直,基本呈平行状分布,同时计算的一次枝晶间距值与Hunt模型计算值接近,实验测得的一次枝晶间距的回归曲线为:y=2843478.3x2-70217.4x+543.8。用温度梯度较高的实验值来模拟时,可得到1λ=26.7G-0.5-135.9,一次枝晶间距λ1与G-0.5基本呈线性关系,这和Hunt模型分析结果一致。     

凝固速率对IN718合金定向凝固组织和偏析行为的影响

采用LMC高梯度定向凝固和微观分析方法,研究了不同凝固速率下IN718合金的凝固组织和偏析行为。结果表明,随着凝固速率的提高,凝固界面由胞晶转为枝晶;一次胞/枝晶间距在凝固速率为20μm/s时提高到最大值257μm,随后逐渐降低,二次枝晶间距明显细化,溶质分配比也随凝固速率的增加而减小。同时,凝固速率增加使糊状区枝晶骨架的渗透性减小,增大了对液体流动的阻碍作用。     

浇注温度对DD6单晶高温合金凝固组织的影响

研究了浇注温度对第二代单晶高温合金DD6凝固组织的影响.结果表明,随着浇注温度的降低,DD6合金的一次枝晶间距稍有增加,枝晶干与枝晶间γ′相尺寸略有增大,γ-γ′共晶相尺寸增加,共晶含量略有减少,合金中主要元素偏析明显减轻.     

冷却过程对AZ61镁合金凝固组织的影响

将AZ61镁合金液态凝固过程分为高温熔体和凝固两个阶段,研究各阶段冷却速率对铸态组织的影响,不同冷速下铸态合金枝晶间距的变化及其对显微硬度的影响。结果表明:在高温熔体阶段随着冷却速率从0.65℃/s增加到15.9℃/s,枝晶组织不断细化且尺寸更均匀,一次枝晶间距从230μm逐渐减小到80μm,二次枝晶间距从12.8μm逐渐减小到9.2μm;凝固阶段在7.8℃/s至23.0℃/s不同冷却速率下,一次枝晶间距从105μm逐渐减小到73μm,二次枝晶间距从10.6μm逐渐减小到8.8μm。两个阶段显微硬度值随冷却速率增大都呈增高趋势。相对于凝固阶段,高温熔体阶段的冷却速率变化对铸态凝固组织的影响更显著。     

凝固参数对ZA27合金次枝晶间距及抗拉强度的影响

ZA27合金是近20年来发展起来的一种新型铸造有色合金,具有较发孤力学性能,但是,由于其很宽的结晶温区、组织多为的树枝晶、导致力学性能下降,本文要用差热分析方法研究DTA曲线特征参数、凝固开始温度和冷却速率对二次枝晶间距λDAS的影响,同时使用IAS－4图像分析系统分析了合金的微观组织,结果表明：DTA曲线峰高与二次枝晶间距线性关系;二次枝晶间距与冷却速率符合指数关系。     

Ni-Ni3Nb共晶合金跃迁变速定向凝固下的组织演化

为了研究非稳态凝固过程对共晶合金凝固组织的影响,在Ni-Ni3Nb共晶合金中采用跃迁变速的方法,研究了凝固组织的变化.研究结果表明:凝固速率从1μm/s跃迁到10μm/s时,共晶层片间距因Ni相分叉而发生细化,跃迁前后的层片间距满足λV1/2=18μm3/2s-1/2;从10μm/s跃迁减速到1μm/s时,共晶层片间距因Ni相合并而发生粗化,粗化过程在一个较长的凝固区域内进行;凝固速率从5μm/s跃迁加速到15μm/s时,部分Ni相失稳长大,而Ni3Nb相保持不变,导致合金凝固组织从规则共晶转变为非规则共晶.     

K5合金高梯度下快速凝固定向组织的研究

本文利用自行研制的超高梯度定向凝固装置（ＺＭＬＭＣ）,考察镍基高温合金Ｋ５在不同的凝固速率下特别是在高速下定向组织的生长行为。研究表明,利用该定向凝固装置可获得完全细化的定向超细晶组织。当Ｒ＝６００ｃｍ／ｓ时,其一次间距λ１小于２３８μｍ;二次枝晶间距被强烈抑制,λ２仅为６６μｍ,组织更加均匀．另外,还对界面稳定性理论在实验条件下进行了讨论     

重力对镍基单晶高温合金枝晶生长和微观偏析的影响

使用长50 m的落管研究Ni-Cr-Al-W-Ta镍基单晶高温合金在重力(1g)和微重力(μg)条件下的凝固行为。用金相显微镜(OM)观察合金的凝固组织并用图像分析软件测量和统计一次和二次枝晶间距,使用扫描电镜能谱(SEM-EDS)测定不同位置枝晶干与枝晶间的化学成分并计算微观偏析系数。结果表明,在重力和微重力条件下这种合金的枝晶特征和合金元素的微观偏析明显不同。重力样品一次和二次枝晶间距比微重力样品的大,随着凝固距离的增大一次枝晶间距的差异变大,而二次枝晶间距的差距变化不大。随着凝固的进行,微重力样品枝晶间Ta、Cr和Al元素的含量呈现先明显升高后略微降低的趋势,W元素含量呈现逐渐下降的趋势,枝晶间液相的密度呈现略微降低的趋势。重力样品枝晶间Ta、Cr和Al元素含量的分布趋势与微重力样品基本相似,W元素含量的分布则与微重力样品明显不同,大部分凝固阶段呈上升趋势,使枝晶间液相的密度沿逆重力方向提高。上述结果表明,在重力条件下凝固前沿溶质密度差导致的对流作用微弱,不是造成枝晶间距增加的主要原因,主要原因应该与凝固前沿热对流造成的温度梯度的降低有关。     

Modelling driver behaviour: a rationale for multivariate statistics

This article provides a discussion of driver behaviour models. Based on a literature survey and using experimental driving simulator data, three types of driver behaviour models are evaluated: motivational models, adaptive control models and trait models. It is shown that motivational models are comprehensive but lack specificity. Many adaptive control models yield precise results but tend to be overly specific and psychologically implausible. Trait models have not been successful in the past and have been criticised for providing post hoc explanations and for the absence of a multifactorial structural approach. We demonstrate that if the right multivariate statistical techniques are used, trait models are suitable for the parsimonious modelling of predictive-valid driver characteristics, thereby taking an intermediate position on a dimension ranging from specific to unspecific models.     

Models for lifetime estimation: an overview with focus on applications to wind turbines

This paper provides an overview of models and methods for estimation of lifetime of technical components. Although the focus in this paper is on wind turbine applications, the major content of the paper is of general nature. Thus, most of the paper content is also valid for lifetime models applied to other technical systems. The models presented and discussed in this paper are classified in different types of model classes. The main classification used in this paper divides the models in the following classes: physical models, stochastic models, data-driven models and artificial intelligence, and combined models. The paper provides an overview of different models for the different classes. Furthermore, advantages and disadvantages of the models are discussed, and the estimation of model parameters is briefly described. Finally, a number of literature examples are given in this paper, providing an overview of applications of different models on wind turbines.     

Classification and Appraisement for Nonparametric Software Reliability Models

A taxonomy of software reliability models is developed that the models are classified as parametric and nonparametric models, and the nonparametric models are classified according to the mathematical methods they used. Then, a practical appraising index system for nonparametric software reliability models are put forward. The nonparametric software reliability models are classified into 5 classes, that is time series analysis models, grey theory forecasting models, artificial neural network models, wavelet ...     

Near-infrared spectroscopic monitoring of a series of industrial batch processes using a bilinear grey model

A good process understanding is the foundation for process optimization, process monitoring, end-point detection, and estimation of the end-product quality. Performing good process measurements and the construction of process models will contribute to a better process understanding. To improve the process knowledge it is common to build process models. These models are often based on first principles such as kinetic rates or mass balances. These types of models are also known as hard or white models. White models are characterized by being generally applicable but often having only a reasonable fit to real process data. Other commonly used types of models are empirical or black-box models such as regression and neural nets. Black-box models are characterized by having a good data fit but they lack a chemically meaningful model interpretation. Alternative models are grey models, which are combinations of white models and black models. The aim of a grey model is to combine the advantages of both black-box models and white models. In a qualitative case study of monitoring industrial batches using near-infrared (NIR) spectroscopy, it is shown that grey models are a good tool for detecting batch-to-batch variations and an excellent tool for process diagnosis compared to common spectroscopic monitoring tools.     

Predicting motor vehicle crashes using Support Vector Machine models

Crash prediction models have been very popular in highway safety analyses. However, in highway safety research, the prediction of outcomes is seldom, if ever, the only research objective when estimating crash prediction models. Only very few existing methods can be used to efficiently predict motor vehicle crashes. Thus, there is a need to examine new methods for better predicting motor vehicle crashes. The objective of this study is to evaluate the application of Support Vector Machine (SVM) models for predicting motor vehicle crashes. SVM models, which are based on the statistical learning theory, are a new class of models that can be used for predicting values. To accomplish the objective of this study, Negative Binomial (NB) regression and SVM models were developed and compared using data collected on rural frontage roads in Texas. Several models were estimated using different sample sizes. The study shows that SVM models predict crash data more effectively and accurately than traditional NB models. In addition, SVM models do not over-fit the data and offer similar, if not better, performance than Back-Propagation Neural Network (BPNN) models documented in previous research. Given this characteristic and the fact that SVM models are faster to implement than BPNN models, it is suggested to use these models if the sole purpose of the study consists of predicting motor vehicle crashes.     

Macro-level pedestrian and bicycle crash analysis: Incorporating spatial spillover effects in dual state count models

This study attempts to explore the viability of dual-state models (i.e., zero-inflated and hurdle models) for traffic analysis zones (TAZs) based pedestrian and bicycle crash frequency analysis. Additionally, spatial spillover effects are explored in the models by employing exogenous variables from neighboring zones. The dual-state models such as zero-inflated negative binomial and hurdle negative binomial models (with and without spatial effects) are compared with the conventional single-state model (i.e., negative binomial). The model comparison for pedestrian and bicycle crashes revealed that the models that considered observed spatial effects perform better than the models that did not consider the observed spatial effects. Across the models with spatial spillover effects, the dual-state models especially zero-inflated negative binomial model offered better performance compared to single-state models. Moreover, the model results clearly highlighted the importance of various traffic, roadway, and sociodemographic characteristics of the TAZ as well as neighboring TAZs on pedestrian and bicycle crash frequency.     

Non-rotational aspherical models of the human optical system

The aim of this work was to define three-dimensional (3D) non-rotational aspherical parametric models for the human cornea and lens using computational geometry and CAD representations. The hyperbolic cosine based function is used for the cornea and a parametric model is used for lens modeling. Data analysis and visualization of 3D non-rotational models were made using the Rhinoceros CAD software and MATLAB software was used for numeric computation. We combined, implemented, and evaluated these models with a 3D ray-tracing in order to fully analyze the human eye model. It was found that 3D non-rotational aspherical models for the human eye could be more accurately modeled and rendered for analysis with finite element method. The objective of this study is to present and analyze mathematical models of the cornea and lens and to highlight the potential of optical applications of the eye models containing astigmatic surfaces, which are more close to the real eye than spherosymmetric eye models.     

A tentative taxonomy for predictive models in relation to their falsifiability

The growing importance of predictive models in biomedical research raises some concerns on the correct methodological approach to the falsification of such models, as they are developed in interdisciplinary research contexts between physics, biology and medicine. In each of these research sectors, there are established methods to develop cause-effect explanations for observed phenomena, which can be used to predict: epidemiological models, biochemical models, biophysical models, Bayesian models, neural networks, etc. Each research sector has accepted processes to verify how correct these models are (falsification). But interdisciplinary research imposes a broader perspective, which encompasses all possible models in a general methodological framework of falsification. The present paper proposes a general definition of 'scientific model' that makes it possible to categorize predictive models into broad categories. For each of these categories, generic falsification strategies are proposed, except for the so-called 'abductive' models. For this category, which includes artificial neural networks, Bayesian models and integrative models, the definition of a generic falsification strategy requires further investigation by researchers and philosophers of science.     

基于等效模型的帽型复合材料加筋壁板优化设计

本工作提出了一种优化复合材料帽型加筋壁板稳定性的有效方法,该方法是基于在PATRAN软件中用单层板来模拟层合板的力学性能的想法。其中用层合板建的加筋板模型称为详细模型,改用单层板建的称为等效模型。对于航空结构中常见的帽型加筋壁板,本工作采用反映实际壁板形状的高精度有限元模型,分别建立了详细模型和等效模型。比较得出两种模型的一阶线性失稳因子和失稳应变基本一致,从而验证了将等效模型用于帽型加筋壁板结构稳定性分析的可行性。为了便于验证该优化方法的有效性,本工作只考虑加筋板的筋条厚度一致的情况。传统方法在优化蒙皮和筋条厚度时需要优化层合板的每个单层的铺层厚度和铺层顺序,本文提出的优化方法只需要解决单层板的厚度优化问题,相比本方法大大减少了设计变量。本工作用此方法优化得到了两个参数不同的复合材料帽型加筋板的最优屈曲性能,发现只需要2～4次迭代优化就可以收敛,收敛速度很快。