The Effect of Pressure Gradient on the Aeroacoustics and WakDynamics of a Finite Wall-Mounted Square Cylinder

This paper reports an experimental investigation of the wake flow structures and noise production of a square finite-wall-mounted cylinder (FWMC) with an aspect ratio of 2.4. The cylinder was immersed in flows with favourable-, near-zero-and adverse-pressure gradients at a Reynolds number of 48000, based on cylinder width. Acoustic and particle image velocimetry measurements were taken simultaneously using the newly developed open-jet pressure gradient test rig in the UNSW Anechoic Wind Tunnel. An adverse pressure gradient was found to enhance the cylinder junction upwash, weaken the free-end downwash and suppress the primary tonal noise at a Strouhal number of approximately 0.1. Conversely, a favourable-pressure gradient promotes downwash over the free-end and leads to a higher tonal noise level. Wake flow structures that correlated with the far-field sound pressure were identified to understand noise generation or suppression mechanisms

The Effect of Pressure Gradient on the Unsteady Surface Pressure and Wake Dynamics of a Finite Wall-Mounted Square Cylinder

An experimental investigation of the wake flow structures and surface pressure fluctuations of a square finite wall-mounted cylinder with an aspect ratio of 2.4 is presented. The cylinder was immersed in flows with favourable, near-zero and adverse pressure gradients at a Reynolds number of 48000, based on cylinder width. Unsteady surface pressure and particle image velocimetry measurements were taken simultaneously on the open-jet pressure gradient test rig in the UNSW Anechoic Wind Tunnel. A favourable pressure gradient was found to reduce the size of the recirculation region in the wake, which intensified the free-end downwash and suppressed the junction upwash. The intensities of the pressure fluctuations are slightly increased at the primary and secondary shedding Strouhal numbers of approximately 0.1 and 0.2. Conversely, the recirculation region expands under an adverse pressure gradient, with the downwash weakened and the upwash enhanced. The peaks in the surface pressure spectra are noticeably attenuated at the primary shedding Strouhal number and completely suppressed at the secondary Strouhal number. Wake flow structures that are correlated with the surface pressure fluctuations were identified to understand the enhancement or suppression mechanisms of the surface pressure fluctuations and the associated shedding regimes

Electronic measurement and control of spin transport in Silicon

The electron spin lifetime and diffusion length are transport parameters that define the scale of coherence in spintronic devices and circuits. Since these parameters are many orders of magnitude larger in semiconductors than in metals, semiconductors could be the most suitable for spintronics. Thus far, spin transport has only been measured in direct-bandgap semiconductors or in combination with magnetic semiconductors, excluding a wide range of non-magnetic semiconductors with indirect bandgaps. Most notable in this group is silicon (Si), which (in addition to its market entrenchment in electronics) has long been predicted a superior semiconductor for spintronics with enhanced lifetime and diffusion length due to low spin-orbit scattering and lattice inversion symmetry. Despite its exciting promise, a demonstration of coherent spin transport in Si has remained elusive, because most experiments focused on magnetoresistive devices; these methods fail because of universal impedance mismatch obstacles, and are obscured by Lorentz magnetoresistance and Hall effects. Here we demonstrate conduction band spin transport across 10 microns undoped Si, by using spin-dependent ballistic hot-electron filtering through ferromagnetic thin films for both spin-injection and detection. Not based on magnetoresistance, the hot electron spin-injection and detection avoids impedance mismatch issues and prevents interference from parasitic effects. The clean collector current thus shows independent magnetic and electrical control of spin precession and confirms spin coherent drift in the conduction band of silicon.Comment: Single PDF file with 4 Figure

Surface Characterisation Based Tool Wear Monitoring in Peripheral milling

The progress of surface metrology in the last decade has led to improved 3D characterisation of surfaces which offers the possibility of monitoring manufacturing operations to give highly detailed information regarding the machine tool condition. This paper presents a case study where areal surface characterisation is used to monitor tool wear in peripheral milling. Due to the fact that tool wear has a direct effect on the machined workpiece surface, the machined surface topography contains much information concerning the machining conditions including the tool wear state. Through analysing the often subtle changes in the surface topography the tool wear state can be highlighted. This paper utilises areal surface characterization, areal auto-correlation function (AACF) and pattern analysis to illustrate the effect of tool wear on the workpiece surface. The result shows that: (1) tool wear, previously difficult to detect will influence almost all of the areal surface parameters; (2) the pattern features of AACF spectrum can reflect the subtle surface texture variation with increasing tool wear. The authors consider that, combined analysis of the surface roughness and its AACF spectrum are a good choice for monitoring the tool wear state especially with the latest developments in on-machine surface metrology

A Bayesian method for evaluating and discovering disease loci associations

Background: A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed Bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi-locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need. Methodology/Findings: We introduce the Bayesian network posterior probability (BNPP) method which addresses the difficulties. The method represents the relationship between a disease and SNPs using a directed acyclic graph (DAG) model, and computes the likelihood of such models using a Bayesian network scoring criterion. The posterior probability of a hypothesis is computed based on the likelihoods of all competing hypotheses. The BNPP can not only be used to evaluate a hypothesis that has previously been discovered or suspected, but also to discover new disease loci associations. The results of experiments using simulated and real data sets are presented. Our results concerning simulated data sets indicate that the BNPP exhibits both better evaluation and discovery performance than does a p-value based method. For the real data sets, previous findings in the literature are confirmed and additional findings are found. Conclusions/Significance: We conclude that the BNPP resolves a pressing problem by providing a way to compute the posterior probability of complex multi-locus hypotheses. A researcher can use the BNPP to determine the expected utility of investigating a hypothesis further. Furthermore, we conclude that the BNPP is a promising method for discovering disease loci associations. © 2011 Jiang et al

Modular metabolic engineering and synthetic coculture strategies for the production of aromatic compounds in yeast.

Microbial-derived aromatics provide a sustainable and renewable alternative to petroleum-derived chemicals. In this study, we used the model yeast Saccharomyces cerevisiae to produce aromatic molecules by exploiting the concept of modularity in synthetic biology. Three different modular approaches were investigated for the production of the valuable fragrance raspberry ketone (RK), found in raspberry fruits and mostly produced from petrochemicals. The first strategy used was modular cloning, which enabled the generation of combinatorial libraries of promoters to optimize the expression level of the genes involved in the synthesis pathway of RK. The second strategy was modular pathway engineering and involved the creation of four modules, one for product formation: RK synthesis module (Mod. RK); and three for precursor synthesis: aromatic amino acid synthesis module (Mod. Aro), p-coumaric acid synthesis module (Mod. p-CA), and malonyl-CoA synthesis module (Mod. M-CoA). The production of RK by combinations of the expression of these modules was studied, and the best engineered strain produced 63.5 mg/L RK from glucose, which is the highest production described in yeast, and 2.1 mg RK/g glucose, which is the highest yield reported in any organism without p-coumaric acid supplementation. The third strategy was the use of modular cocultures to explore the effects of division of labor on RK production. Two two-member communities and one three-member community were created, and their production capacity was highly dependent on the structure of the synthetic community, the inoculation ratio, and the culture media. In certain conditions, the cocultures outperformed their monoculture controls for RK production, although this was not the norm. Interestingly, the cocultures showed up to 7.5-fold increase and 308.4 mg/L of 4-hydroxy benzalacetone, the direct precursor of RK, which can be used for the semi-synthesis of RK. This study illustrates the utility of modularity in synthetic biology tools and their applications to the synthesis of products of industrial interest