Inhibition of stress corrosion cracking in 304 stainless steel through titanium ion implantation

The effects of titanium ion implantation on the stress corrosion cracking (SCC) behaviour of 304 austenitic stainless steel were studied. Slow strain rate tests (SSRTs) were conducted on 304 steel in air and in 5?wt-% NaCl solution. The microscopic effects of ion implantation were evaluated by Stopping and Range of Ions in Matter Procedures (SRIM). Fracture morphologies and microstructures were investigated by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The fracture surfaces illustrate that ion implantation significantly inhibits the corrosion pits that initiate SCC. A dense passive film, which inhibits SCC, was formed during the ion implantation process. SCC initiation was restrained due to the dense dislocation nets that were generated by titanium ion implantation.

Highlights

• Ion implantation inhibits SCC susceptibility.

• The lack of Cr at the grain boundary leads to the expansion of SCC along the grain boundary.

• Implantation-induced damage leads to high-density dislocations.

• The surface was amorphised due to high-density dislocations.

     

Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques

Accurate and rapid assessment of the as-built status on any construction site provides the opportunity to understand the current performance of a project easily and quickly. Rapid project assessment further identifies discrepancies between the as-built and as-planned progress, and facilitates decision making on the necessary remedial actions. Currently, manual visual observations and surveying are the most dominant data capturing techniques but they are time-consuming, error-prone, and infrequent, making quick and reliable decision-making difficult. Therefore, research on new approaches that allow automatic recognition of as-built performance and visualization of construction progress is essential. This paper presents and compares two methods for obtaining point cloud models for detection and visualization of as-built status for construction projects: (1) A new method of automated image-based reconstruction and modeling of the as-built project status using unordered daily construction photo collections through analysis of Structure from Motion (SfM); (2) 3D laser scanning and analysis of the as-built dense point cloud models. These approaches provide robust means for recognition of progress, productivity, and quality on a construction site. In this paper, an overview of the newly developed automated image-based reconstruction approach and exclusive features which distinct it from other image-based or conventional photogrammetric techniques is presented. Subsequently the terrestrial laser scanning approach carried out for reconstruction and comparison of as-built scenes is presented. Finally the accuracy and usability of both of these techniques for metric reconstruction, automated production of point cloud models, 3D CAD shape modeling, and as-built visualizations is evaluated and compared on eight different case studies. It is shown that for precise defect detection or alignment tasks, image-based point cloud models may not be as accurate and dense as laser scanners' point cloud models. Nonetheless image-based point cloud models provide an opportunity to extract as-built semantic information (i.e., progress, productivity, quality and safety) through the content of the images, are easy to use, and do not need add burden on the project management teams by requiring expertise for data collection or analysis. Finally image-based reconstruction automatically provides photo alignment with point cloud models and enables image-based renderings which can remarkably impact automated performance monitoring and as-built visualizations.     

Dynamic analysis and split range control for maximization of operating range of continuous microbial fuel cell

Human development is inherently connected with availability of water and energy. Energy production requires water, whereas water treatment needs energy. On the other hand, microbial fuel cell has capability to produce energy and water simultaneously from waste water or organic matter. In this paper, first principle-based model of variable volume microbial fuel cell is simulated. Hydraulic retention time is selected as the manipulated variable using the study of steady state and dynamic responses. Classical PI and model predictive control strategies are developed for controlling the produced power from the cell, and its performance is tested for servo problem. Settling time for positive and negative set points is found to be 126 and 889 h in case of classical PI and 120 and 750 h in case of linear MPC, respectively along with large increase (three times order of magnitude) in working volume for negative set point. These control challenges are overcome by using split range controller with variable and constant volume microbial fuel cells. The settling time for negative set point is found to be 49 and 21 h for classical PI and linear MPC schemes, respectively, which is significantly lower than using only variable volume microbial fuel cell. Also, there is no increase in the working volume of the constant volume microbial fuel cell. Hence, operating range of the microbial fuel cell is enhanced using split range controller.     

Model study of a fuel cell range extender for a neighborhood electric vehicle (NEV)

Fuel cell systems have the potential to provide high-efficiency, low-cost power for Neighborhood Electric Vehicles (NEVs). Model results are presented examining the utility of placing a hydrogen PEM fuel cell on-board a Miles Electric ZX40ST work truck NEV as an “after-market add-on” range extender to the existing battery electric drive train, thereby creating a NEVx. Through the development and use of the Sandia-Miles-Altergy Range Test (SMART) model, we have examined the potential for a number of PEM fuel cell stack systems (with varying output power), combined with various hydrogen storage and electrical storage system configurations to achieve the desired range extension for a single 8-h work shift. Furthermore, we have evaluated the “well-to-wheels” (WTW) and greenhouse gas (GHG) reductions, and assessed the costs (both capital and O&M) for the different NEVx configurations and operating profiles.     

Modeling of a scanning laser range sensor for robotic applications

The geometric and noise modeling of a scanning range sensor is described. The parameters of the model are determined from data collected during the calibration of the real sensor. The models are shown to predict range scan results which accurately match those of the real sensor.     

Consumer context-specific sensory acceptance tests: Effects of a cognitive warm-up on affective product discrimination

For simple hedonic ratings, product range effects often limit the reliability and accuracy of consumer affective discriminations among multiple products. In the present study, a cognitive warm-up (CWU) procedure was applied to consumer acceptance tests in order to stabilize consumers’ evaluative criteria by evoking their personal evaluation context. The effects of such procedure on the robustness of the affective product discriminations were investigated using two sets of comparison experiments, each using a related samples design. Consumers with equivalent sensory preference profile for skin lotions were screened and divided into High Reflection Thinkers (HRT) and Low Reflection Thinkers (LRT) using the Cognitive Reflection Test. They were then randomly, but evenly assigned to either a treatment (Group I) or a control (Group II) group, in which hedonic rating was performed either with or without the preceding CWU procedure, thus forming four sub-groups (treatment group-HRT, treatment group-LRT, control group-HRT, and control group-LRT subjects). For each sub-group, discriminability between the two chosen products in terms of d′ value was compared across the two experimental conditions consisting of different product ranges. The results indicated that only in the control group-LRT subjects, the two conditions displayed different product discrimination. It suggests that for LRT subjects who are assumed to be more intuitive and thus might be more vulnerable to such product range effects, the CWU had a stabilizing effect on the evaluative criteria, resulting in more robust product discrimination.     

四腿菱形变截面梁微加速度计结构设计与研究

在传统等截面双边四梁微加速度计结构上,以变极距式电容传感器为对象,设计一种菱形变截面梁。为增加梁的稳定性,满足加速度计对不同方向刚度要求,并提高灵敏度,建立加速度计数学理论模型,推导出灵敏度与刚度的关系。建立梁的纯弯曲模型,并基于静电驱动原理,推导出静电刚度公式,确定满足静电刚度小于机械刚度的条件下加速度计量程。结果显示,菱形变截面梁减小了梁的应力集中,使梁两方向的刚度最大化,提高了加速度计灵敏度。     

A crowd enabled approach for processing nearest neighbor and range queries in incomplete databases with accuracy guarantee

With the proliferation of mobile devices and wireless technologies, location based services (LBSs) are becoming popular in smart cities. Two important classes of LBSs are Nearest Neighbor (NN) queries and range queries that provide user information about the locations of point of interests (POIs) such as hospitals or restaurants. Answers of these queries are more reliable and satisfiable if they come from trustworthy crowd instead of traditional location service providers (LSPs). We introduce an approach to evaluate NN and range queries with crowdsourced data and computation that eliminates the role of an LSP. In our crowdsourced approach, a user evaluates LBSs in a group. It may happen that group members do not have knowledge of all POIs in a certain area. We present efficient algorithms to evaluate queries with accuracy guarantee in incomplete databases. Experiments show that our approach is scalable and incurs less computational overhead.     

基于模糊控制器的移动机器人导航

针对复杂的道路环境，利用传感器探测到的距离信息，采取基于行为的控制结构，设计了一种基于模糊控制器的移动机器人实时导航算法，实时调整机器人的位姿，使机器人能够在避免与道路边界碰撞的前提下沿着道路顺利前进。结合典型机器人传感器的分布特征，利用MobotSim机器人仿真软件对算法进行了仿真，证明了算法的可行性和有效性。