The effect of operating conditions on post-injection fuel discharge in an optical engine

After the end of injection, the needle closes and residual fuel present inside the injector sac and orifices is discharged due to the high fluid inertia. This so-called post-injection fuel discharge can present several problems. The excess fuel can undergo incomplete combustion due to its large, slow moving and often surface-bound nature. Not only does this have a negative effect on emissions and performance, but it has been speculated that the by-products of incomplete combustion are implicated in the growth of carbonaceous deposits on the tips of fuel injectors. Accumulation of these deposits is known to lead to premature fuel injector failure that can lead to re-ductions in power output and engine lifetime. Seeing as modern multiple-injection strategies give rise to an in-creased number of transient injection phases, post-injection discharges are an increasingly common occurrence during normal operating conditions. In order to develop a phenomenological model for the fluid dynamics after the end of injection, there is a need to characterise the causes of this discharge and how they might be influenced by engine operating conditions. In this study we present ongoing analysis into results from the first visualisation of post injection fuel discharge at the microscopic level under engine-like operating conditions. We observed the process of fuel discharge for multi-hole injectors, using a high-speed camera fitted with a long-distance micro-scope and a high-speed laser illumination source. We related the effect of a variety of operating conditions on the severity of this process, including injection pressure and in-cylinder pressure along with a characterisation of the dynamics of the various modes by which these undesired liquid structures are produced. We present the different modes by which this process occurs and we conclude that the extent of post-injection discharge depends on both the in-cylinder and injection pressures

Transverse joint configuration development and testing for a modular bridge deck replacement system

According to the 2009 Report Card for America\u27s Infrastructure, one in four of the nation\u27s bridges are listed as structurally deficient or functionally obsolete, establishing a dire need for new and innovative repair and replacement techniques to improve the efficiency and state of the nation\u27s bridges. Full-depth pre-cast bridge deck panels have proven to be a rapid, efficient and cost effective solution to cast-in-place bridge deck replacement techniques. However, the panel-to-panel connection for these segmental deck replacement systems requires further development and testing to improve their structural redundancy, and to streamline their installation procedure. Multiple transverse joint configurations have been developed, fabricated, and tested in order to evaluate their shear transfer capabilities and ease of use. It was determined that a round corrugated transverse joint configuration provided the greatest shear transfer capacity and an efficient installation procedure

Crack detection in concrete tunnels using a gabor filter invariant to rotation

Producción CientíficaIn this article, a system for the detection of cracks in concrete tunnel surfaces, based on image sensors, is presented. Both data acquisition and processing are covered. Linear cameras and proper lighting are used for data acquisition. The required resolution of the camera sensors and the number of cameras is discussed in terms of the crack size and the tunnel type. Data processing is done by applying a new method called Gabor filter invariant to rotation, allowing the detection of cracks in any direction. The parameter values of this filter are set by using a modified genetic algorithm based on the Differential Evolution optimization method. The detection of the pixels belonging to cracks is obtained to a balanced accuracy of 95.27%, thus improving the results of previous approaches.Ministerio de Economía y Competitividad under project Ref. IPT-2012-0980-370000Ministerio de Ciencia e Innovación, research project Ref. DPI2014-56500Junta de Castilla y León Ref. VA036U14

A deep learning approach towards railway safety risk assessment

Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks

A method for in-field railhead crack detection using digital image correlation

Railway infrastructure managers must decide when and how to maintain rails. However, they often have insufficient information about railhead cracks. Therefore, we propose a new method for rail crack detection using a train-mounted digital image correlation (DIC) camera system. The measurement train\u27s weight cause rail bending, allowing the DIC to measure strain concentrations caused by surface-breaking cracks. In this study, we evaluate the method under laboratory conditions. The detected cracks correlate to the actual crack network in the analysed rail field sample. Furthermore, finite element simulations show the method\u27s high sensitivity to crack depths. Existing methods, such as ultra-sonic and eddy-current, produce damage severity indications. The proposed method complements these techniques by providing a discrete description of the surface-breaking cracks and their depth. This information enables infrastructure managers to optimize rail maintenance. Additionally, such detailed measurements can be valuable for research in railhead damage evolution

ToScA North America (6 – 8 June 2017, The University of Texas, Austin, TX) Program

ToScA North America will address key areas of science, including Multi-modal Imaging, Geosciences, Forensics, Increasing Contrast, Educational Outreach, Data, Materials Science and Medical and Biological Science.University of Texas High-Resolution X-ray CT Facility (UTCT); Jackson School of Geosciences, The University of Texas at Austin; Natural History Museum (London); Royal Microscopical Society (Oxford, UK)Geological Science

Verification of the use of micro-CT scanning to assess the features of entire squat type defects

Squats and studs are defects in railheads that share features, but have different causes. This paper examined four squat and stud samples from three different traffic environments to compare features using μ-CT X-ray scans, surface and subsurface inspection. μ-CT scanning has been used before as a non-destructive method to investigate rail defects, but not the entire defect. The scans were verified and allowed the identification of areas of interest when sectioning the samples further. The scan volumes were also used to create 3D models of the crack networks for the 3 samples that were scanned. All defects contain similar superficial features but the depth and severity of the subsurface damage varies. This work provides a visualisation of the 3D nature of studs in a way not seen before, as a 3D model the crack network from an in-service defect. The models of two of the defects showed the influence of hollow wheels initiating defects, as the crack seemed to initiate on the field side, grow down and towards the gauge side, before resurfacing as the longitudinal crack noted in all four defect samples. One sample is believed to have initiated due to contamination of the weld and the only squat sample, which failed in track, was believed to be ingot cast steel containing many inclusions. Three samples were studs and one was a squat. Each defect developed for different reasons, although the two metro samples were similar. One of the studs shows branching of cracks that, based on its changing angle of growth, could continue to grow into transverse defects, breaking the rail. The three defects that were scanned would all be classed as studs, but their crack morphology varies, possibly because they are all from different traffic environments. They also show slight differences to other studs in literature