The propagation of pressure in a gelled waxy oil pipeline as studied by particle imaging velocimetry

Paraffinic crude oils in pipelines may form waxy gels during flow shutdowns. These gels can be dislodged by applying pressure if the wall shear stress, proportional to the local pressure gradient, exceeds the gel yield stress. The simplest models assume that the axial pressure profile becomes linear immediately after a jump in upstream pressure, but this fails to account for gel time‐dependent rheology or the effect of gel voids on pressure wave propagation. To investigate the former factor, pressure profile and particle imaging velocimetry (PIV) measurements were performed on a model oil gelled under pressure to reduce void formation. After a jump in upstream pressure to a value insufficient to restart flow, the axial pressure profile becomes linear in a two‐step process, with an immediate small rise in downstream pressure followed by a time‐delayed jump. The local downstream gel deformation measured by PIV exhibits similar two‐step time dependence. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Comparative study of two CPU router time management algorithms in cellular IP networks

Providing good quality of service (QoS) in cellular IP networks is an important requirement for performance improvement of the cellular IP network. Resource reservation is one of the methods used in achieving this goal and is proven to be effective. The main resources to be reserved in a cellular IP network are bandwidth, buffer and central processing unit (CPU) cycles. Router CPU cycle is the time taken by the router to process the packet of the flow before forwarding it to the next router (hop). This paper proposes a model for CPU cycle optimization of routers for real‐time flows in a cellular IP network. The model applies both genetic algorithm (GA) and particle swarm optimization (PSO) as soft computing tools to optimize the CPU cycles and reduces the flow processing time at each router in the route taken by a flow. Simulation experiments illustrate a comparative study of the model.     

Quantizing gravity using physical states of a superstring

A symmetric zero-mass tensor of rank two is constructed using the superstring modes of excitation, which satisfies the physical state constraints of a superstring. These states have a one to one correspondence with the quantized field operators and are shown to be the absorption and emission quanta of the Minkowski space Lorentz tensor, using the quantum field theory method of quantization. The principle of equivalence makes the tensor identical to the metric tensor at any arbitrary space-time point. The propagator for the quantized field is deduced. The gravitational interaction is switched on by going over from ordinary derivatives to co-derivatives. The Riemann-Christoffel affine connections are calculated, and the weak field Ricci tensor R _μν ⁰ is shown to vanish. The interaction part R _μν ^int is found, and the exact R _μν of the theory of gravity is expressed in terms of the quantized metric. The quantum-mechanical self-energy of the gravitational field in vacuum is shown to vanish. By the use of a projection operator, it is shown that gravitons are quanta of the general relativity field which gives the Einstein equation G _μν = 0. It is suggested that quantum gravity may be renormalizable by the use of the massless ground state of this superstring theory for general relativity, and a tachyonic vacuum creates and annihilates quanta of quantized gravitational field.     

Cold Model Investigations of Melting of Ice in a Gas-Stirred Vessel

The melting of steel scrap in high temperature liquid iron melt is investigated by conducting cold model experiments of the melting of ice sample of different geometries and sizes in an argon-stirred vessel containing water. The melting process of ice samples is observed using a high-speed camera. Design of experiments is based on similarity criteria. The relationships between non-dimensional groups related to heat transfer (Nu, Re, Pr, and Gr) are derived for different experimental conditions. The results are compared with those reported in the literature. The heat transfer coefficient is estimated as a function of mixing power and is found to be in good agreement with the calculated values obtained by using reported relationships in literature.     

A Physics-driven Neural Networks-based Simulation System (PhyNNeSS) for multimodal interactive virtual environments involving nonlinear deformable objects

BACKGROUND: While an update rate of 30 Hz is considered adequate for real time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. METHODS: In this work we present PhyNNeSS - a Physics-driven Neural Networks-based Simulation System - to address this long-standing technical challenge. The first step is an off-line pre-computation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. RESULTS: We present realistic simulation examples from interactive surgical simulation with real time force feedback. As an example, we have developed a deformable human stomach model and a Penrose-drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. CONCLUSIONS: A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based pre-computational step allows training of neural networks which may be used in real time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal Interactive Simulation) for general use.     

The mean squared error of Geweke and Porter-Hudak's estimator of the memory parameter of a long-memory time series

We establish some asymptotic properties of a log-periodogram regression estimator for the memory parameter of a long-memory time series. We consider the estimator originally proposed by Geweke and Porter-Hudak (The estimation and application of long memory time series models. Journal of Time Ser. Anal. 4 (1983), 221–37). In particular, we do not omit any of the low frequency periodogram ordinates from the regression. We derive expressions for the estimator's asymptotic bias, variance and mean squared error as functions of the number of periodogram ordinates, m , used in the regression. Consistency of the estimator is obtained as long as m ←∞ and n ←∞ with ( m log m )/ n ← 0, where n is the sample size. Under these and the additional conditions assumed in this paper, the optimal m , minimizing the mean squared error, is of order O( n ^4/5). We also establish the asymptotic normality of the estimator. In a simulation study, we assess the accuracy of our asymptotic theory on mean squared error for finite sample sizes. One finding is that the choice m = n ^1/2, originally suggested by Geweke and Porter-Hudak (1983), can lead to performance which is markedly inferior to that of the optimal choice, even in reasonably small samples.     

Interaction of Bacillus polymyxa with some oxide minerals with reference to mineral beneficiation and environmental control

Interaction of Bacillus polymyxa with calcite, hematite, corundum and quartz resulted in significant surface chemical changes not only of the cells but also in the minerals. Both the cell surfaces as well as quartz particles were rendered more hydrophobic after mutual interaction, whilst the rest of the minerals exhibited enhanced hydrophilicity after interaction with the bacteria. The bacteria were also observed to be capable of dissolving calcite, hematite and corundum and biosorbing the dissolved metal ions to varying extents. An excess of polysaccharides could be observed on biotreated calcite, hematite and corundum while the predominance of a protein-based metabolic product was evident on quartz surfaces. The utility of bioprocessing in the beneficiation of the above minerals through bioflotation and bioflocculation is demonstrated.