AN EXPERIMENTAL INVESTIGATION OF THE GOVERNING PROPERTIES IN A VORTEX TUBE

Although awareness of the phenomenon of temperature separation in Ranque-Hilsch vortex tubes dates back at least nine decades, some mystery surrounding the phenomenon remains to this day. These devices split an incoming stream of fluid into two streams—one with a greater total temperature than the incoming fluid and the other with a lower total temperature. This temperature separation is accomplished with no moving parts and no external sources of energy including heat transfer to or from the device. In attempts to understand the physics of the temperature separation, previous researchers have characterized the effect through various inlet temperatures and pressures as well as various gases with different properties. Unfortunately, the findings documented in the literature are sometimes inconsistent indicating the possibility that previously uncontrolled properties and flow conditions govern temperature separation to an unappreciated degree. In the present research, two new flow characteristics are examined for their role in temperature separation—volumetric heat capacity, ρC_p, and nozzle velocity. In the present experiments with air, it was found that by matching nozzle velocity and ρC_p—even with disparate pressures, temperatures, Reynolds numbers, and Mach numbers—the resulting temperature separation curves are identical. This is the first known documentation of such a finding. The results suggest that nozzle velocity is fundamental to scaling the performance of a vortex tube, while the nozzle volumetric heat capacity is also relevant to its behavior.

Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit

Background Large streamed datasets, characteristic of life science applications, are often resource-intensive to process, transport and store. We propose a pipeline model, a design pattern for scientific pipelines, where an incoming stream of scientific data is organized into a tiered or ordered “data hierarchy". We introduce the HASTE Toolkit, a proof-of-concept cloud-native software toolkit based on this pipeline model, to partition and prioritize data streams to optimize use of limited computing resources. Findings In our pipeline model, an “interestingness function” assigns an interestingness score to data objects in the stream, inducing a data hierarchy. From this score, a “policy” guides decisions on how to prioritize computational resource use for a given object. The HASTE Toolkit is a collection of tools to adopt this approach. We evaluate with 2 microscopy imaging case studies. The first is a high content screening experiment, where images are analyzed in an on-premise container cloud to prioritize storage and subsequent computation. The second considers edge processing of images for upload into the public cloud for real-time control of a transmission electron microscope. Conclusions Through our evaluation, we created smart data pipelines capable of effective use of storage, compute, and network resources, enabling more efficient data-intensive experiments. We note a beneficial separation between scientific concerns of data priority, and the implementation of this behaviour for different resources in different deployment contexts. The toolkit allows intelligent prioritization to be `bolted on' to new and existing systems – and is intended for use with a range of technologies in different deployment scenarios.

Improvement of a multiphase flow model for wellhead chokes under critical and subcritical conditions using field data

The characterization of the multiphase flow through valves and orifices is a problem yet to be solved in engineering design, and there is a need for a prediction model able to simulate the complexity of this kind of flow in relation to fluid thermodynamic behaviour, and applicable to different incoming stream conditions and compositions. The present paper describes the development of a global model for the calculation of the discharge coefficient of orifices and choke valves operating under two- and three-phase flow as well as critical and subcritical conditions. The model generalizes the hydrovalve model developed by Selmer-Olsen et al. (in: Wilson (ed) Proceedings of 7th international conference on Multiphase Production, BHR Group, pp 441–446, 1995) and the Henry–Fauske (J Heat Transfer 93: 179–187, 1971. 10.1115/1.3449782) non-equilibrium model on the basis of an updated definition of the discharge coefficient. The model has been adapted to real choke valve geometries, by fitting the discharge coefficient and model parameters using field data from three production wells. The model developed is a global quartic function with different constants for the different valve geometries. The new discharge coefficient allows to simulate field data with high accuracy.

What’s on the Human Mind? Decision Theory and Deterrence

Indeed, deterrence, as Freedman and Mazarr recount in this volume in respectively Chaps. 10.1007/978-94-6265-419-8_1 and 10.1007/978-94-6265-419-8_2, aims to dissuade an opponent from taking undesirable actions. Clear communication of demands (a red line for instance), coupled with a credible threat to inflict pain if necessary, and demonstration of resolve are some obvious essential elements for creating effective deterrence. Success crucially also depends on whether the opponent receives the intended signal, interprets it as intended, and has the perception that the message is congruent with reality, i.e., that the opponent can make good on her threats. Success furthermore assumes that the demands communicated are acceptable. If those prerequisites exist, theory suggests a rational actor will back down, after weighing the benefits of the envisioned actions versus the potential costs that may result when the threat is executed. This chapter offers a synthesis of insights that have appeared since the 1980s that fundamentally challenge that assumption of rationality. This contribution about the workings of the human mind concerns the various filters and cognitive shortcuts that colour the incoming stream of information and the processes to digest it and come to a decision.

Framework for opinion as a service on review data of customer using semantics based analytics

At Opinion mining plays a significant role in representing the original and unbiased perception of the products/services. However, there are various challenges associated with performing an effective opinion mining in the present era of distributed computing system with dynamic behaviour of users. Existing approaches is more laborious towards extracting knowledge from the reviews of user which is further subjected to various rounds of operation with complex procedures. The proposed system addresses the problem by introducing a novel framework called as Opinion-as-a-Service which is meant for direct utilization of the extracted knowledge in most user friendly manner. The proposed system introduces a set of three sequential algorithm that performs aggregated of incoming stream of opinion data, performing indexing, followed by applying semantics for extracting knowledge. The study outcome shows that proposed system is better than existing system in mining performance.

The dynamics of buffered and triggered selection from RSVP streams

From an incoming stream of visual information, only a limited number of stimuli can be selected for extensive processing. Much of the literature assumes that selection of cued items in RSVP streams is a result of attentional sampling being triggered by the cue. We provide evidence for another process - selection from a buffer of stimulus representations. This can yield selection of stimuli presented before the cue, despite the common theoretical claim that such stimuli should be unavailable. Our novel statistical method provides quantitative evidence that stimuli presented before the cue are sometimes selected. This phenomenon occured when two RSVP streams were presented simultaneously and one was cued at a random time. When the number of streams was increased, evidence for pre-cue reports diminished and selection was delayed. These results indicated that stimuli in RSVP evoke representations that persist long enough to be selected, provided attention is fast enough. The speed and variability of temporal selection of items is affected by endogenous attention and possibly by competition among stimulus representations. In conditions with fewer streams, faster selection and buffering may occur thanks to participants applying endogenous attention before the cue is presented, speeding the response to the cue and leading to more reports of the item before the cue.

The Flow Noise Calculation for an Axisymmetric Body in a Complex Underwater Environment

The flow noise of a sonar platform is one of the main background interferences for sonar applications. This paper focuses on the flow noise of an axisymmetric body in a complex oceanic environment. Under the condition of a constant stream velocity which comes from the axial direction, an analytical method for computing the flow noise power spectrum in the transition region of the axisymmetric body is given in detail. The flow noise power spectrum computed by the analytical method is in agreement with the numerical simulation result. Then the flow noise physical features of the axisymmetric body in different incoming stream directions and velocity states caused by the complex oceanic environment are computed and analyzed by the numerical method. The results show that as the incoming stream direction changes, the transition region will migrate and the flow noise radiation direction of the axisymmetric body will also rotate at an angle which equals the stream direction variation. The flow noise energy generated by other directional incoming streams is slightly larger than that generated by the stream coming from an axial direction. When the incoming stream velocity is time-varying, the vorticity change on the axisymmetric body surface is obviously stronger than that under a constant stream, and the generated flow noise energy is also significantly larger. In addition, it indicates that there is a significant correlation between the intensity of flow noise energy and the magnitude of flow velocity.

The Second Thermal Wonder of the World–A Storm Appears In Both Hemispheres- -Strong Snit Theory Proof–Global Warming--China Dust Problem

Debris streams of exploding stars produce hotspots on Earth’s surface. These hotspots have maximum energy near the time of initial impact with the surface of the Earth and this higher energy has been recorded by space satellites. The incoming stream of positive particles reacts with Earth’s magnetic field that produces a magnetic bottle. As the Earth revolves in its orbit, the Earth tilt causes the incoming streams velocity vector to move across the Earth’s vernal equinox so that where the stream touches the surface is in different hemispheres. The incoming new impact streams can merge causing more energy to be delivered to a particular area and produce extreme weather events like the warm Alaskan winters of 2017-18 and 2018-19. The figures presented of these phenomena are more direct proof of the SNIT theory. The data are available to determine exploding star strike frequency. It is possible to identify an exploding star by knowing its declination and using the time the storm switches hemisphere locations. China’s dust problem can be avoided.

Experimental Analysis on Processing of Unbounded Data

Processing of unordered and unbounded data is the prime requirement of the current businesses. Large amount of rapidly generated data demands the processing of the same without the storage and as per the timestamp associated with it. It is difficult to process these unbounded data with batch engine as the existing batch systems suffer from the delay intrinsic by accumulating entire incoming records in a group prior to process it. However windowing can be useful when dealing with unbounded data which pieces up a dataset into fixed chunks for processing with repeated runs of batch engine. Contrast to batch processing, stream handling system aims to process information that is gathered in a little timeframe. In this way, stream data processing ought to be coordinated with the flow of data. In the real world the event time is always skewed with the processing time which introduce issues of delay and completeness in incoming stream of data. In this paper, we presented the analysis on the watermark and trigger approach which can be used to manage these unconventional desires in the processing of unbounded data.

Clustering Based Approach for Novelty Detection in Text Documents

As the information is overloaded over the internet accessing of information from the internet according to a given query provides redundant and irrelevant information. It is necessary to retrieve relevant and novel information from a given query by the user. With the result of this the user will require minimum effort to access the information need. In this work we proposed a clustering based approach for novelty detection which will provide the relevant and novel documents for the information need. Based on the user query the incoming stream of documents will be clustered using k-means algorithm. Then the cluster heads are selected from the various clusters with the minimum distance. These cluster heads are the novel documents from a collection of documents from different clusters having the large distance. The proposed technique can be further used in the field of information retrieval.