Large-eddy simulation of cross-flow around ship sections

The present work is motivated by phenomena occurring in the flow field around ship-like bodies with an incoming lateral flow (cross-flow, 90 ° drift angle). Three-dimensional unsteady flows around different ship sections are investigated by using computational fluid dynamic (CFD) tools with large-eddy simulation (LES) subgrid-scale turbulence model. The simulation results are compared to measurements at several Reynolds numbers in the 90–200,000 range. Focus in our investigation is on the characterization of the motion of vortex structures generated by the separated flow. Another target in the study is to obtain better knowledge of the hydrodynamic forces acting on the sections. Computed pressure and drag coefficients are compared with experimental measurements. The comparison between simulations and measurements shows that an LES model can predict the flow field around ship sections in detail and the hydrodynamic forces acting on the sections.     

Engine performance and toxic gas analysis of biobutanol-blended gasoline as a vehicle fuel

A comprehensive study evaluating the performance of biobutanol-blended gasoline in passenger cars was conducted because biobutanol is considered a better biofuel than bioethanol as it has no water solubility and it has a higher caloric value, giving it a higher energy value. Several kinds of samples—suboctane gasoline, 8 volume percentage and 16 volume percentage biobutanol—blended gasoline, and a 10 volume percentage MTBE-blended market sample (as the oxygencontaining gasoline)-were tested to evaluate the engine performance in terms of the detergency of the intake valves and combustion chambers, power, emissions, and fuel efficiency. Additionally, the toxicity of the emissions from these biobutanolblended samples was tested in order to assess the viability of biobutanol as one of the competitive potential substitutes for MTBE as an oxygenator in the near future. The results show that biobutanol-blended gasoline samples had relatively better detergency, relatively higher power, and similar levels of emissions compared with those of MTBE-blended gasoline. Formaldehyde was emitted from all of the samples at almost the same levels and within the error range, whereas biobutanolblended gasoline samples emitted approximately three times the amount of acetaldehyde than did the suboctane gasoline. This study shows that biobutanol is one of the best alternative bioalcohol fuels for use in the near future.     

CFD-based method of determining form factor <Emphasis Type="Italic">k</Emphasis> for different ship types and different drafts

The value of form factor k at different drafts is important in predicting full-scale total resistance and speed for different types of ships. In the ITTC community, most organizations predict form factor k using a low-speed model test. However, this method is problematic for ships with bulbous bows and transom. In this article, a Computational Fluid Dynamics (CFD)-based method is introduced to obtain k for different type of ships at different drafts, and a comparison is made between the CFD method and the model test. The results show that the CFD method produces reasonable k values. A grid generating method and turbulence model are briefly discussed in the context of obtaining a consistent k using CFD.     

Employability skills of maritime business graduates: industry perspectives

The maritime industry underpins international business and world trade. As to be expected, business management is critical for the maritime industry, requiring highly trained individuals and teams to lead the development, implementation and control of sound contemporary management practices. Maritime business degrees are developed by universities to meet such demand by providing graduates with sufficient skills for the onshore business-related roles. This empirical study conducted in Australia, USA and Canada, investigates current and future industry employability skills for maritime business graduates through focus groups, individual interviews and an online survey with senior managers in maritime organisations. This study found the important employability skills for maritime business graduates which include communication, problem solving, adaptability, self-management, team work, and digital literacy and technology. Demand for digital literacy and technology knowledge and skills have increased due to the maritime industry having a trend of moving towards digitalisation and automation. However, the survey findings revealed that a skills focus for maritime business graduates will not be the technology itself but the use and management of technology. In relation to future skills/knowledge required from maritime business degree graduates in 10 years’ time, communication and adaptability are recognised as being the most important. Employers expect that maritime business graduates should be able to adopt new technology and be competent in communication, and be more adaptable given the highly dynamic nature of the maritime industry. Moreover, they require graduates to be equipped with a higher level of computer skills, have a strong work ethic and multilingual skills.     

A time‐staged multi‐objective network‐design model

A multi‐objective, time‐staged network‐design problem is formulated. Through transformation, the problem is decomposed into a set of single‐period, single‐objective problems. Lexicographic ordering is instrumental in effecting this transformation; it also allows a backward‐recursion algorithm to be applied using strong pruning criteria. Furthermore, monotonicity properties enable us to solve the problem using the familiar tree‐search algorithms. The solution method has several desirable properties — as shown by an example and a case study of Tripoli Province, Libya. First, the algorithm ensures continuity of project implementation over the multi time‐periods and provides optimality in later computational stages irrespective of the decision at an interim stage. Second, the algorithm tends to provide accessibility to unconnected regions in the study area at low user‐cost without employing weights to the two objective functions of accessibility and user‐cost efficiency. Such a property is deemed advantageous for suggesting transportation investments based purely on purchasing the greatest benefit for each dollar, with political neutrality strictly maintained.     

A method to evaluate equitable accessibility: combining ethical theories and accessibility-based approaches

In this paper, we present the case that traditional transport appraisal methods do not sufficiently capture the social dimensions of mobility and accessibility. However, understanding this is highly relevant for policymakers to understand the impacts of their transport decisions. These dimensions include the distribution of mobility and accessibility levels over particular areas or for specific population groups, as well as how this may affect various social outcomes, including their levels of participation, social inclusion and community cohesion. In response, we propose a method to assess the socially relevant accessibility impacts (SRAIs) of policies in some of these key dimensions. The method combines the use of underlying ethics principles, more specifically the theories of egalitarianism and sufficientarianism, in combination with accessibility-based analysis and the Lorenz curve and Gini index. We then demonstrate the method in a case study example. Our suggestion is that policymakers can use these ethical perspectives to determine the equity of their policies decisions and to set minimum standards for local transport delivery. This will help them to become more confident in the development and adoption of new decision frameworks that promote accessibility over mobility and which also disaggregate the costs and benefits of transport policies over particular areas or for specific under-served population groups.     

A traffic flow simulator for short‐term travel time forecasting

This paper presents an off‐line forecasting system for short‐term travel time forecasting. These forecasts are based on the historical traffic count data provided by detectors installed on Annual Traffic Census (ATC) stations in Hong Kong. A traffic flow simulator (TFS) is developed for short‐term travel time forecasting (in terms of offline forecasting), in which the variation of perceived travel time error and the fluctuations of origin‐destination (O‐D) demand are considered explicitly. On the basis of prior O‐D demand and partial updated detector data, the TFS can estimate the link travel times and flows for the whole network together with their variances and covariances. The short‐term travel time forecasting by O‐D pair can also be assessed and the O‐D matrix can be updated simultaneously. The application of the proposed off‐line forecasting system is illustrated by a numerical example in Hong Kong.     

A data model applied to the knowledge-based ICAS

Image-guided computer aided surgery system (ICAS) contributes to safeness and success of surgery operations by means of displaying anatomical structures and showing correlative information to surgeons in the process of operation. Based on analysis of requirements for ICAS, a new concept of clinical knowledge-based ICAS was proposed. Designing a reasonable data structure model is essential for realizing this new concept. The traditional data structure is limited in expressing and reusing the clinical knowledge such as locating an anatomical object, topological relations of anatomical objects and correlative clinical attributes. A data structure model called mixed adjacency lists by octree-path-chain (MALOC) was outlined, which can combine patient's images with clinical knowledge, as well as efficiently locate the instrument and search the objects' information. The efficiency of data structures was analyzed and experimental results were given in comparison to other traditional data structures. The result of the nasal surgery experiment proves that MALOC is a proper model for clinical knowledge-based ICAS that has advantages in not only locating the operative instrument precisely but also proving surgeons with real-time operation-correlative information. It is shown that the clinical knowledge-based ICAS with MALOC model has advantages in terms of safety and success of surgical operations, and help in accurately locating the operative instrument and providing operation-correlative knowledge and information to surgeons in the process of operations.     

Uncertainty analysis of value for money assessment for public-private partnership projects

In recent years, private sectors are encouraged to take an active part in franchising of urban infrastructure investments and operations, which promotes the rapid development of public-private partnership (PPP) in infrastructure and public service supply. Value for money (VFM) assessment has been officially proposed to provide a reference for selection of projects planning to adopt PPP. Based on the bottlenecks of VFM application in China and the uncertainties for urban infrastructure PPP projects, a discounted cash flow (DCF) model is established for VFM of infrastructure PPP projects. Then, a Monte Carlo simulation model is established on the basis of uncertainty factors for VFM. Through the analysis of Huai’an trams PPP project, coping strategies of uncertainties for VFM are put forward. Findings of the research may propel the establishment of a complete VFM evaluation system for PPP projects. Key instructional functions of VFM during the process of decision-making can be brought into full play and PPP may develop orderly.     

Automatic semantic analysis of software requirements through machine learning and ontology approach

Nowadays, software requirements are still mainly analyzed manually, which has many drawbacks (such as a large amount of labor consumption, inefficiency, and even inaccuracy of the results). The problems are even worse in domain analysis scenarios because a large number of requirements from many users need to be analyzed. In this sense, automatic analysis of software requirements can bring benefits to software companies. For this purpose, we proposed an approach to automatically analyze software requirement specifications (SRSs) and extract the semantic information. In this approach, a machine learning and ontology based semantic role labeling (SRL) method was used. First of all, some common verbs were calculated from SRS documents in the E-commerce domain, and then semantic frames were designed for those verbs. Based on the frames, sentences from SRSs were selected and labeled manually, and the labeled sentences were used as training examples in the machine learning stage. Besides the training examples labeled with semantic roles, external ontology knowledge was used to relieve the data sparsity problem and obtain reliable results. Based on the SemCor and WordNet corpus, the senses of nouns and verbs were identified in a sequential manner through the K-nearest neighbor approach. Then the senses of the verbs were used to identify the frame types. After that, we trained the SRL labeling classifier with the maximum entropy method, in which we added some new features based on word sense, such as the hypernyms and hyponyms of the word senses in the ontology. Experimental results show that this new approach for automatic functional requirements analysis is effective.