Dysprosium transport in Nd-Fe-B pellets

The addition of heavy rare earth (RE) elements to Nd2Fe14B based magnets to form (Nd,Dy)2Fe14B is known to increase the coercivity and high temperature performance required for hybrid vehicle electric motors and other extreme temperature applications. Attempts to conserve heavy rare earth elements for high temperature (RE)2Fe14B based magnets have led to the development of a grain boundary diffusion process for bulk magnets. This process relies on transport of a heavy rare earth, such as Dy, into a bulk Nd2Fe14B magnet along pores, a low volume fraction of eutectic liquid along grain boundary grain triple junctions and grain boundaries. This enriches the grain surfaces in Dy through the thickness of the bulk magnet, leading to larger increases coercivity with a smaller Dy concentration than can be achieved with homogeneous alloys. Attempts to carry out the same process during sintering require significant control of Dy transport efficiency. The macroscopic transport of Dy in Nd2.7Fe14B1.4 based powder packs is studied using a 'layered' pellet, where Nd2.7Fe14B1.4powder is an interlayer and Dy source as a center layer. The sintering of this layered pellet provided evidence for very large effective diffusion lengths aided by Dy rich liquid flow through connected porosity. Approaches to controlling Dy transportation include decreasing the liquid phase transport capability of the powder pack by increasing the melting point of the Dy source and the decreasing amount of RE rich liquid in the powder packs. The solid-liquid reaction is studied in which melt spun Nd2.7Fe14B1.4 ribbons are PVD coated with Dy-Fe eutectic composition and then thermally treated. The resulting microstructure from the reaction between Dy-Fe eutectic coating and Nd2.7Fe14B1.4 ribbon is interpreted as support for a proposed dissolution/reprecipitation process between solid and liquid phases. The estimate the diffusion coefficient and the effective diffusion length of Dy sources in Nd2.7Fe14B1.4 layered pellets and melt spun ribbons were obtained from the calculation of Fick's second law combined with EDS results from the experiment. The results indicate that the effective diffusion coefficient of Dy in the layered pellets is higher than the diffusion in ribbons due to its higher porosity than ribbons.

A comparative study of the structural, mechanical and tribological characteristics of TiSiC-Cr coatings prepared in CH4 and C2H2 reactive atmosphere by cathodic vacuum arc

TiSiC-Cr coatings, with Cr and Si as additional elements, were deposited on Si, C 45 and 316 L steel substrates via cathodic arc evaporation. Two series of coatings with thicknesses in the range of 3.6–3.9 μm were produced, using either CH4 or C2H2 as carbon containing gas. For each series, different coatings were prepared by varying the carbon rich gas flow rate between 90 and 130 sccm, while maintaining constant cathode currents (110 and 100 A at TiSi and Cr cathodes, respectively), substrate bias (–200 V) and substrate temperature (∼320 °C). The coatings were analyzed for their mechanical characteristics (hardness, adhesion) and tribological performance (friction, wear), along with their elemental and phase composition, chemical bonds, crystalline structure and cross-sectional morphology. The coatings were found to be formed with nano-scale composite structures consisting of carbide crystallites (grain size of 3.1–8.2 nm) and amorphous hydrogenated carbon. The experimental results showed significant differences between the two coating series, where the films formed from C2H2 exhibited markedly superior characteristics in terms of microstructure, morphology, hardness, friction behaviour and wear resistance. For the coatings prepared using CH4, the measured values of crystallite size, hardness, friction coefficient and wear rate were in the ranges of 7.2–8.2 nm, 26–30 GPa, 0.3–0.4 and 2.1–4.8 × 10−6 mm3 N−1 m−1, respectively, while for the coatings grown in C2H2, the values of these characteristics were found to be in the ranges of 3.1–3.7 nm, 41–45 GPa, 0.1–0.2 and 1.4–3.0 × 10−6 mm3 N−1 m−1, respectively. Among the investigated coatings, the one produced using C2H2 at the highest flow rate (130 sccm) exhibited the highest hardness (45.1 GPa), the lowest friction coefficient (0.10) and the best wear resistance (wear rate of 1.4 × 10−6 mm3 N−1 m−1).

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

The S.T.A.B. Trial - Standardised Testing of Artificial Blood. A comparative study of various products that may be used as artificial blood for high fidelity simulation training in the critical care setting

Aim: In the current climate of medical education, there is an ever-increasing demand for and emphasis on simulation as both a teaching and training tool. The objective of our study was to compare the realism and practicality of a number of artificial blood products that could be used for high-fidelity simulation. Method: A literature and internet search was performed and 15 artificial blood products were identified from a variety of sources. One product was excluded due to its potential toxicity risks. Five observers, blinded to the products, performed two assessments on each product using an evaluation tool with 14 predefined criteria including color, consistency, clotting, and staining potential to manikin skin and clothing. Each criterion was rated using a five-point Likert scale. The products were left for 24 hours, both refrigerated and at room temperature, and then reassessed. Statistical analysis was performed to identify the most suitable products, and both inter- and intra-rater variability were examined. Results: Three products scored consistently well with all five assessors, with one product in particular scoring well in almost every criterion. This highest-rated product had a mean rating of 3.6 of 5.0 (95% posterior Interval 3.4-3.7). Inter-rater variability was minor with average ratings varying from 3.0 to 3.4 between the highest and lowest scorer. Intrarater variability was negligible with good agreement between first and second rating as per weighted kappa scores (K = 0.67). Conclusion: The most realistic and practical form of artificial blood identified was a commercial product called KD151 Flowing Blood Syrup. It was found to be not only realistic in appearance but practical in terms of storage and stain removal.

Indoor environments : design, productivity and health

In a typical large office block, by far the largest lifetime expense is the salaries of the workers - 84% for salaries compared with : office rent (14%), total energy (1%), and maintenance (1%). The key drive for business is therefore the maximisation of the productivity of the employees as this is the largest cost. Reducing total energy use by 50% will not produce the same financial return as 1% productivity improvement? The aim of the project which led to this review of the literature was to understand as far as possible the state of knowledge internationally about how the indoor environment of buildings does influence occupants and the impact this influence may have on the total cost of ownership of buildings. Therefore one of the main focus areas for the literature has been identifying whether there is a link between productivity and health of building occupants and the indoor environment. Productivity is both easy to define - the ratio of output to input - but at the same time very hard to measure in a relatively small environment where individual contributions can influence the results, in particular social interactions. Health impacts from a building environment are also difficult to measure well, as establishing casual links between the indoor environment and a particular health issue can be very difficult. All of those issues are canvassed in the literature reported here. Humans are surprisingly adaptive to different physical environments, but the workplace should not test the limits of human adaptability. Physiological models of stress, for example, accept that the body has a finite amount of adaptive energy available to cope with stress. The importance of, and this projects' focus on, the physical setting within the integrated system of high performance workplaces, means this literature survey explores research which has been undertaken on both physical and social aspects of the built environment. The literature has been largely classified in several different ways, according to the classification scheme shown below. There is still some inconsistency in the use of keywords, which is being addressed and greater uniformity will be developed for a CD version of this literature, enabling searching using this classification scheme.

Defining the flammability of cylindrical metal rods through characterization of the thermal effects of the ignition promoter

All relevant international standards for determining if a metallic rod is flammable in oxygen utilize some form of “promoted ignition” test. In this test, for a given pressure, an overwhelming ignition source is coupled to the end of the test sample and the designation flammable or nonflammable is based upon the amount burned, that is, a burn criteria. It is documented that (1) the initial temperature of the test sample affects the burning of the test sample both (a) in regards to the pressure at which the sample will support burning (threshold pressure) and (b) the rate at which the sample is melted (regression rate of the melting interface); and, (2) the igniter used affects the test sample by heating it adjacent to the igniter as ignition occurs. Together, these facts make it necessary to ensure, if a metallic material is to be considered flammable at the conditions tested, that the burn criteria will exclude any region of the test sample that may have undergone preheating during the ignition process. A two-dimensional theoretical model was developed to describe the transient heat transfer occurring and resultant temperatures produced within this system. Several metals (copper, aluminum, iron, and stainless steel) and ignition promoters (magnesium, aluminum, and Pyrofuze®) were evaluated for a range of oxygen pressures between 0.69 MPa (100 psia) and 34.5 MPa (5,000 psia). A MATLAB® program was utilized to solve the developed model that was validated against (1) a published solution for a similar system and (2) against experimental data obtained during actual tests at the National Aeronautics and Space Administration White Sands Test Facility. The validated model successfully predicts temperatures within the test samples with agreement between model and experiment increasing as test pressure increases and/or distance from the promoter increases. Oxygen pressure and test sample thermal diffusivity were shown to have the largest effect on the results. In all cases evaluated, there is no significant preheating (above about 38°C/100°F) occurring at distances greater than 30 mm (1.18 in.) during the time the ignition source is attached to the test sample. This validates a distance of 30 mm (1.18 in.) above the ignition promoter as a burn length upon which a definition of flammable can be based for inclusion in relevant international standards (that is, burning past this length will always be independent of the ignition event for the ignition promoters considered here. KEYWORDS: promoted ignition, metal combustion, heat conduction, thin fin, promoted combustion, burn length, burn criteria, flammability, igniter effects, heat affected zone.

Can B.I.M be civil?

After many years of development BIM (Building Information Modelling) is starting to achieve significant penetration into the building sector of the construction industry. This paper describes the current status of BIM and the drivers that are motivating the change from 2D CAD to BIM within the building sector. The paper then discusses what the implications of the technology underlying BIM may be for the civil construction sector of the construction industry. A project carried out by the Cooperative Research Centre for Construction Innovation is used as an example of this technology as well as several international examples.

Modelling the effects of bone fragment contact in fracture healing

The fracture healing process is modulated by the mechanical environment created by imposed loads and motion between the bone fragments. Contact between the fragments obviously results in a significantly different stress and strain environment to a uniform fracture gap containing only soft tissue (e.g. haematoma). The assumption of the latter in existing computational models of the healing process will hence exaggerate the inter-fragmentary strain in many clinically-relevant cases. To address this issue, we introduce the concept of a contact zone that represents a variable degree of contact between cortices by the relative proportions of bone and soft tissue present. This is introduced as an initial condition in a two-dimensional iterative finite element model of a healing tibial fracture, in which material properties are defined by the volume fractions of each tissue present. The algorithm governing the formation of cartilage and bone in the fracture callus uses fuzzy logic rules based on strain energy density resulting from axial compression. The model predicts that increasing the degree of initial bone contact reduces the amount of callus formed (periosteal callus thickness 3.1mm without contact, down to 0.5mm with 10% bone in contact zone). This is consistent with the greater effective stiffness in the contact zone and hence, a smaller inter-fragmentary strain. These results demonstrate that the contact zone strategy reasonably simulates the differences in the healing sequence resulting from the closeness of reduction.

Crowd Counting Using Multiple Local Features

In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.

Knowledge discovery for adaptive negotiation agents in e-marketplaces

Intelligent software agents are promising in improving the effectiveness of e-marketplaces for e-commerce. Although a large amount of research has been conducted to develop negotiation protocols and mechanisms for e-marketplaces, existing negotiation mechanisms are weak in dealing with complex and dynamic negotiation spaces often found in e-commerce. This paper illustrates a novel knowledge discovery method and a probabilistic negotiation decision making mechanism to improve the performance of negotiation agents. Our preliminary experiments show that the probabilistic negotiation agents empowered by knowledge discovery mechanisms are more effective and efficient than the Pareto optimal negotiation agents in simulated e-marketplaces.