Numerical study of turbulent fluid flow and heat transfer in lateral perforated extended surfaces

Numerical study has been performed in this study to investigate the turbulent convection heat transfer on a rectangular plate mounted over a flat surface. Thermal and fluid dynamic performances of extended surfaces having various types of lateral perforations with square, circular, triangular and hexagonal cross sections are investigated. RANS (Reynolds averaged Navier–Stokes) based modified k–ω turbulence model is used to calculate the fluid flow and heat transfer parameters. Numerical results are compared with the results of previously published experimental data and obtained results are in reasonable agreement. Flow and heat transfer parameters are presented for Reynolds numbers from 2000 to 5000 based on the fin thickness.

Functional MRI BOLD response to Tower of London performance of first-episode schizophrenia patients using cortical pattern matching

Due to its three-dimensional folding pattern, the human neocortex; poses a challenge for accurate co-registration of grouped functional; brain imaging data. The present study addressed this problem by; employing three-dimensional continuum-mechanical image-warping; techniques to derive average anatomical representations for coregistration; of functional magnetic resonance brain imaging data; obtained from 10 male first-episode schizophrenia patients and 10 age-matched; male healthy volunteers while they performed a version of the; Tower of London task. This novel technique produced an equivalent; representation of blood oxygenation level dependent (BOLD) response; across hemispheres, cortical regions, and groups, respectively, when; compared to intensity average co-registration, using a deformable; Brodmann area atlas as anatomical reference. Somewhat closer; association of Brodmann area boundaries with primary visual and; auditory areas was evident using the gyral pattern average model.; Statistically-thresholded BOLD cluster data confirmed predominantly; bilateral prefrontal and parietal, right frontal and dorsolateral; prefrontal, and left occipital activation in healthy subjects, while; patients’ hemispheric dominance pattern was diminished or reversed,; particularly decreasing cortical BOLD response with increasing task; difficulty in the right superior temporal gyrus. Reduced regional gray; matter thickness correlated with reduced left-hemispheric prefrontal/; frontal and bilateral parietal BOLD activation in patients. This is the; first study demonstrating that reduction of regional gray matter in; first-episode schizophrenia patients is associated with impaired brain; function when performing the Tower of London task, and supports; previous findings of impaired executive attention and working memory; in schizophrenia.

Dewatering of microalgal culture for biodiesel production : exploring polymer flocculation and tangential flow filtration

Background: Conventional biodiesel production relies on trans-esterification of lipids extracted from vegetable crops. However, the use of valuable vegetable food stocks as raw material for biodiesel production makes it an unfeasibly expensive process. Used cooking oil is a finite resource and requires extra downstream processing, which affects the amount of biodiesel that can be produced and the economics of the process. Lipids extracted from microalgae are considered an alternative raw material for biodiesel production. This is primarily due to the fast growth rate of these species in a simple aquaculture environment. However, the dilute nature of microalgae culture puts a huge economic burden on the dewatering process especially on an industrial scale. This current study explores the performance and economic viability of chemical flocculation and tangential flow filtration (TFF) for the dewatering of Tetraselmis suecicamicroalgae culture. Results: Results show that TFF concentrates the microalgae feedstock up to 148 times by consuming 2.06 kWh m-3 of energy while flocculation consumes 14.81 kWhm-3 to concentrate the microalgae up to 357 times. Economic evaluation demonstrates that even though TFF has higher initial capital investment than polymer flocculation, the payback period for TFF at the upper extreme ofmicroalgae revenue is ∼1.5 years while that of flocculation is ∼3 years. Conclusion: These results illustrate that improved dewatering levels can be achieved more economically by employing TFF. The performances of these two techniques are also compared with other dewatering techniques.

Preparation of macroporous methacrylate monolithic material with convective flow properties for bioseparation : Investigating the kinetics of pore formation and hydrodynamic performance

The preparation of macroporous methacrylate monolithic material with controlled pore structures can be carried out in an unstirred mould through careful and precise control of the polymerisation kinetics and parameters. Contemporary synthesis conditions of methacrylate monolithic polymers are based on existing polymerisation schemes without an in-depth understanding of the dynamics of pore structure and formation. This leads to poor performance in polymer usage thereby affecting final product recovery and purity, retention time, productivity and process economics. The unique porosity of methacrylate monolithic polymer which propels its usage in many industrial applications can be controlled easily during its preparation. Control of the kinetics of the overall process through changes in reaction time, temperature and overall composition such as cross-linker and initiator contents allow the fine tuning of the macroporous structure and provide an understanding of the mechanism of pore formation within the unstirred mould. The significant effect of temperature of the reaction kinetics serves as an effectual means to control and optimise the pore structure and allows the preparation of polymers with different pore size distributions from the same composition of the polymerisation mixture. Increasing the concentration of the cross-linking monomer affects the composition of the final monoliths and also decreases the average pore size as a result of pre-mature formation of highly cross-linked globules with a reduced propensity to coalesce. The choice and concentration of porogen solvent is also imperative. Different porogens and porogen mixtures present different pore structure output. Example, larger pores are obtained in a poor solvent due to early phase separation.

Convergence-guaranteed time-varying RRT path planning for profiling floats in 4-Dimensional flow

This paper presents an extension to the Rapidly-exploring Random Tree (RRT) algorithm applied to autonomous, drifting underwater vehicles. The proposed algorithm is able to plan paths that guarantee convergence in the presence of time-varying ocean dynamics. The method utilizes 4-Dimensional, ocean model prediction data as an evolving basis for expanding the tree from the start location to the goal. The performance of the proposed method is validated through Monte-Carlo simulations. Results illustrate the importance of the temporal variance in path execution, and demonstrate the convergence guarantee of the proposed methods.

On the use of optical flow for scene change detection and description

We propose the use of optical flow information as a method for detecting and describing changes in the environment, from the perspective of a mobile camera. We analyze the characteristics of the optical flow signal and demonstrate how robust flow vectors can be generated and used for the detection of depth discontinuities and appearance changes at key locations. To successfully achieve this task, a full discussion on camera positioning, distortion compensation, noise filtering, and parameter estimation is presented. We then extract statistical attributes from the flow signal to describe the location of the scene changes. We also employ clustering and dominant shape of vectors to increase the descriptiveness. Once a database of nodes (where a node is a detected scene change) and their corresponding flow features is created, matching can be performed whenever nodes are encountered, such that topological localization can be achieved. We retrieve the most likely node according to the Mahalanobis and Chi-square distances between the current frame and the database. The results illustrate the applicability of the technique for detecting and describing scene changes in diverse lighting conditions, considering indoor and outdoor environments and different robot platforms.

Topical pattern based document modelling and relevance ranking

For traditional information filtering (IF) models, it is often assumed that the documents in one collection are only related to one topic. However, in reality users’ interests can be diverse and the documents in the collection often involve multiple topics. Topic modelling was proposed to generate statistical models to represent multiple topics in a collection of documents, but in a topic model, topics are represented by distributions over words which are limited to distinctively represent the semantics of topics. Patterns are always thought to be more discriminative than single terms and are able to reveal the inner relations between words. This paper proposes a novel information filtering model, Significant matched Pattern-based Topic Model (SPBTM). The SPBTM represents user information needs in terms of multiple topics and each topic is represented by patterns. More importantly, the patterns are organized into groups based on their statistical and taxonomic features, from which the more representative patterns, called Significant Matched Patterns, can be identified and used to estimate the document relevance. Experiments on benchmark data sets demonstrate that the SPBTM significantly outperforms the state-of-the-art models.

Multi-action recognition via stochastic modelling of optical flow and gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.

Analysis of passenger group behaviour and its impact on passenger flow using an agent-based model

Group interaction within crowds is a common phenomenon and has great influence on pedestrian behaviour. This paper investigates the impact of passenger group dynamics using an agent-based simulation method for the outbound passenger process at airports. Unlike most passenger-flow models that treat passengers as individual agents, the proposed model additionally incorporates their group dynamics as well. The simulation compares passenger behaviour at airport processes and discretionary services under different group formations. Results from experiments (both qualitative and quantitative) show that incorporating group attributes, in particular, the interactions with fellow travellers and wavers can have significant influence on passengers activity preference as well as the performance and utilisation of services in airport terminals. The model also provides a convenient way to investigate the effectiveness of airport space design and service allocations, which can contribute to positive passenger experiences. The model was created using AnyLogic software and its parameters were initialised using recent research data published in the literature.

A modified Density-Based Scanning Algorithm with Noise for spatial travel pattern analysis from Smart Card AFC data

Smart Card Automated Fare Collection (AFC) data has been extensively exploited to understand passenger behavior, passenger segment, trip purpose and improve transit planning through spatial travel pattern analysis. The literature has been evolving from simple to more sophisticated methods such as from aggregated to individual travel pattern analysis, and from stop-to-stop to flexible stop aggregation. However, the issue of high computing complexity has limited these methods in practical applications. This paper proposes a new algorithm named Weighted Stop Density Based Scanning Algorithm with Noise (WS-DBSCAN) based on the classical Density Based Scanning Algorithm with Noise (DBSCAN) algorithm to detect and update the daily changes in travel pattern. WS-DBSCAN converts the classical quadratic computation complexity DBSCAN to a problem of sub-quadratic complexity. The numerical experiment using the real AFC data in South East Queensland, Australia shows that the algorithm costs only 0.45% in computation time compared to the classical DBSCAN, but provides the same clustering results.